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Electronics design engineers are well aware of lithium-ion’s shortcomings. So, the upcoming battery revolution revolves around experimental materials in novel applications to reduce price, resource scarcity, and environmental impact.

Which options are the most viable as lithium mining and production ramp up, becoming one of the most noteworthy yet contradictory global markets of the climate-cognizant age? Below is a sneak peek at three most viable alternatives to lithium-ion batteries.

1. Solid-state batteries

Solid-state gigafactories are expanding to the West because of their unique compositions and promise to remove flammability concerns from liquid electrolytes. The problems plague energy storage and electric vehicle (EV) adoption, but solid-state alternatives promise a longer lifecycle and improved safety.

These batteries have another advantage over lithium-ion options because they minimize thermal runaway, one of the most prominent concerns when justifying lithium’s costs and labor investments. Li-ion batteries charge to 100% in two hours, giving electronics design experts a welcome challenge to innovate past this already incredible engineering feat.

The design benefits electronics design engineers by improving power density while still being lightweight. So, solid-state blueprints may require some lithium but in severely reduced capacities to reduce reliance. Enough solid-state battery variances exist to forge more sustainable anodes and cathodes, such as lithium-iron phosphates and polymers.

2. Metal-air batteries

Metal-air batteries rely on oxygen as the cathode material, using a reduction reaction for power. Using oxygen removes barriers regarding storage and accessibility problems in other battery structures.

Professionals can use zinc, aluminum, iron and more to minimize exploiting the world’s limited lithium stores and get a denser battery. They are five to 30 times more energy efficient than li-ion products, leveraging materials more easily found and obtained in nature. This could empower other technologies to more sustainable futures, including hearing aids and uninterruptible power supplies (UPSs).

3. Sodium-ion batteries

Sodium is a low-cost and potent material for batteries, and it doesn’t need nickel or cobalt to work. Supply chains need more abundant materials to maintain B2B relationships and meet market demands. It is not as well-known in the industry, but sodium could solve many of the quandaries electronic design engineers are trying to solve, including:

• Cost-effectiveness
• Improved safety
• High energy density
• Coulombic efficiency
• Heightened durability
• Decreased environmental impact

Even though sodium provides these advantages, it must overrun lithium as the incumbent. Despite lithium’s faults, the sector set it as the gold standard for batteries. Sodium must prove how much it can pack into a smaller battery than li-ions.

Sodium-ion battery density is already outperforming lower-tier products at a faster pace. The first sodium-powered vehicle will make its debut in January 2024. It has a 157-mile range and 25 kWh capacity, which is impressive given it’s the first of its kind.

What’s after lithium-ion

Other technologies like flow batteries and magnesium-ion options are also rising in the electronics design landscape. This scratches the surface of unharnessed energetic potential for renewable power storage and electric vehicle applications.

Engineers must consider every detail—from microprocessors to passive components—when prototyping new circuit designs. Attention to detail will make embedded system development run smoothly, leading to compliant, futuristic batteries for a greener planet.

Ellie Gabel is a freelance writer as well as an associate editor at Revolutionized.

Related Content

• All About Batteries
• How to design a battery management system
• How to structure a battery management system
• Introduction to lithium-ion rechargeable battery design
• Will EV Battery Design Developments Help or Hinder the Second-Life Battery Market?

The post Beyond lithium-ion: Exploring next-gen battery technologies appeared first on EDN.

Automated inline handler systems for in-circuit testers rely on numerous sensors
fitted around the system to detect and control mechanical operations.

The programmable logic controller (PLC) runs a set of complex algorithms that supervise all handler operations and maintain control of safeguards. This ensures consistent and safe operation of the system. To prevent any external interruption to its operation, the PLC does not allow any user intervention to its process once it starts the automated operation. As such, most of the automated inline systems have a closed-loop design that does not allow user customization of the handler operation.

However, it is possible to split the PLC’s algorithms into sections without degrading the robustness of the safeguards. In doing so, it opens little windows of opportunities into the PLC’s control of the handler and allows customization of the handler operations to suit user requirements.

My previous post shared some of the test steps in the i7090 plug-in for Pathwave Test Executive for Manufacturing (PTEM) application which allow you to configure the i7090 system handler profile and monitor the error codes during runtime. Figure 1 below recounts the flowchart that I discussed before.

Figure 1: Flowchart for handler configuration and error code monitoring

From the flowchart in Figure 1, configuration import is an optional process as user can also set up the handler profile directly on the i7090 system handler application itself. You may choose to omit the configuration import and go directly to the next stage in the process.

The next stage of the handler operation includes setting the handler into auto mode operation followed by the runtime processes. By combining the configuration import sequence into a single bubble and adding the auto mode and runtime processes, the updated flowchart is as shown in Figure 2. Since configuration import is optional, the flow can go directly into the Auto mode process after the handler control is connected to PTEM.

Figure 2: Auto mode and runtime processes follows configuration import processes.

Switching the handler into auto mode lets the PLC take over command of the handler operations and execute the handler initialization algorithm. During the initialization process, you can continue to monitor the handler error codes and terminate the operation if required. However, you cannot alter the initialization steps that the PLC dictates. In this post, I will share details of the events during the initialization process when you set the handler to auto mode. We will leave the runtime processes to the next post.

Getting into the auto mode operation is a two-step process.

First, switch the handler into auto operation by turning the selector switch on the machine panel from Manual to Auto as shown in Figure 3.

Figure 3: Set handler into auto mode by turning switch into auto position on the machine panel.

The next step is to start the auto mode operation in the PLC. In the PTEM testplan shown in Figure 4, I created a repeat loop to monitor the selector switch using the Handler_IsSwitchInAutoMode test step and waited for it to enter the auto position. Within the loop, I included a print step to display a text message and prompt the operator to make the switch. Once the selector switch is in auto, the repeat loop exits and calls the Handler_StartAutoMode test step to start auto mode operation at the PLC.

Figure 4: Handler_StartAutoMode triggers the auto mode operation.

In auto mode operation, the handler starts with the initialization of the hardware before going into the board transfer stage to bring in the device under test (DUT). For initialization to happen, the handler must not be in any error state, and the fixture must be correctly locked down, with all doors closed. No foreign items, including any DUT, should be in the test bay area or on the fixture. Make sure to remove any tools or equipment from the test bay before triggering auto mode operation. Monitor the initialization sequences as the testplan executes, to confirm that the handler receives the correct commands before leaving it to run on its own.

Initialization starts with the press, conveyor, and stopper returned to their home positions. The first action to expect after going into auto mode is that the press should start to move upwards, followed by the conveyor rail and stopper. During the process, the PLC monitors all the position sensors in the handler and reports errors if any one of them fails to respond correctly. The completion of these movements is confirmation that the mechanical hardware is functioning well.

Next, the handler configures the width of the conveyor to the targeted setting in the system handler application. You will notice the handler moves the rear conveyor outwards to its home position, and then inwards until it reaches the targeted width. If adjustment is successful, the adjusted width matches that of the DUT, else it is an indication that you may have set the parameters wrong, or the adjustment motors are defective.

The final phase of the initialization process is getting the press ready at the standby height position. From its home position at the top, the press moves downwards and halts at the standby height position. Standby height is the position of the press where it is slightly above the DUT. This gives sufficient clearance for the DUT to pass under it and move to the stopper position. Moving the press downwards from the standby height is more efficient than having the press to travel from the home position at the top. This reduces the time it takes to engage the DUT into the fixture. Once the press reaches standby height, the handler is now ready to receive DUT. Once the upstream conveyor presents a DUT to the handler, the transfer will begin automatically.

The testplan is now in the parallel operation process where it is constantly monitoring for errors and waiting for the DUT to get into position. Once a DUT is in position, the testplan continues into the runtime operation, which we will discuss in the next post.

Table 1 tabulates all the test steps that I have discussed so far in all my posts on automation control of the i7090 system. You can also check out my previous post on Keysight i7090 with PTEM where I shared how the error monitoring sequences can be created easily with PTEM. Meanwhile, if you have questions or comments regarding what you have just read, feel free to send me a message.

Kwan Wee Lee Technical Marketing Engineer Keysight Technologies

The post Keysight i7090 with PTEM System initialization appeared first on ELE Times.

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This is my first soldering iron I bought it for 15€ , solder wire is from grandpa , and the rest is from electronic scrap bin in my school. Not the best project (a 12 year old could do it [I am 18] )but for the first time soldering I don't consider it to be the worst. submitted by /u/SimonIngot [link] [comments]

In the worldwide economy, private equity firms are essential given that they provide funding to companies at different phases of growth. In 2023, the invested private equity capital amounted to 1.8 trillion U.S. dollars.

Effective deal flow management, however, presents serious difficulties for private equity firms. In this article, we’ll examine the main challenges associated with overseeing deal flow in private equity and talk about potential solutions to get around them.

Portfolio Management: Maximizing Value Creation

Private equity companies constantly have to oversee their investment portfolios so that they maximize value creation and provide stakeholders with attractive returns. This means maintaining a close eye on the performance of the portfolio companies, offering operational support and strategic direction, and recognizing chances for expansion and value addition.Solution: Portfolio Management Software for Venture Capital

Investors and/or managers can use portfolio management software for venture capital planned with private equity investors’ demands in mind to manage their portfolios profitably. With the use of tools like portfolio analytics, benchmarking, performance monitoring, and scenario planning, these types of software enable businesses to evaluate the performance and overall health of their assets and make data-driven choices that improve portfolio returns.

Software for portfolio management can also help with collaboration and communication with the management teams of portfolio companies, which can facilitate reporting procedures and support agreement on strategic goals.

Image sourceDeal Sourcing: Navigating the Sea of Opportunities

Finding top-notch investment prospects is one of the main issues in deal flow management. Private equity companies have plenty of possible acquisitions at their disposal. However, they must navigate a large pool of prospects to find those that correspond to their investment criteria and strategic goals. This approach, which calls for in-depth study, networking, and due diligence, can take a long time and a lot of resources.

Solution: Advanced Screening and Filtering Tools

Private equity companies have the power to utilize elaborate screening and filtering techniques to optimize their deal sourcing processes. Based on predetermined criteria, these tools find and rank viable investment possibilities using data analytics, machine learning, and artificial intelligence. By optimizing the first screening procedure, companies increase efficiency and boost success rates, allowing them to concentrate their time and resources on assessing the most promising prospects.

Deal Evaluation: Assessing Risk and Potential Returns

Systematically assessing each deal’s possible risks and profits comes next once possible opportunities for investment have been found. Professionals in private equity are required to perform rigorous due diligence, examine the target company’s management team and development perspective, research financial data, and assess market trends. Establishing wise investment decision-making steps in this process necessitates thorough thought and competent judgment.

Solution: Data-Driven Decision-Making

Private equity companies can use methodologies for data-driven decision-making for better deal appraisal processes. Businesses can gain insight into deal dynamics, pinpoint the main forces underlying value creation, and mitigate risk by utilizing sophisticated analytics and financial modeling tools. Additionally, the integration of scenario analysis and stress testing could assist in the quantification of uncertainties and provide more trustworthy guidance for investment decisions.

Image source

Regulatory Compliance: Navigating Legal and Regulatory Frameworks

Another major problem for private equity companies managing deal flow is navigating the regulatory and legal landscapes. To lessen legal risks and guarantee the integrity of deals, compliance with a multitude of laws and regulations is crucial, including tax laws, securities laws, and anti-money laundering requirements. For private equity businesses and their investors, violations of regulatory norms can have devastating implications, harm to their brand, and even legal ramifications.

Solution: Robust Compliance Processes and Due Diligence

Due diligence and effective compliance processes are to be implemented by private equity companies in order to cope with regulatory compliance difficulties. This includes employing legal counsel with knowledge of relevant regulations and regulatory frameworks, inspecting regulatory risks connected to suggested deals, and doing extensive legal due diligence on target organizations. Investing in compliance management software could be helpful in tracking regulatory advancements, automating compliance monitoring, and certifying adherence to legal and regulatory obligations at every stage of the deal lifecycle.

Conclusion

For private equity firms looking to detect and seize profitable investments, deal flow management is an essential endeavor. Private equity professionals could boost efficiency, mitigate risks, and foster sustainable value creation throughout their investment portfolios by utilizing creative ideas and technology-driven techniques to handle critical concerns including deal sourcing, appraisal, execution, and portfolio management.

The post Deal Flow Management in Private Equity: Key Challenges and Solutions appeared first on Electronics Lovers ~ Technology We Love.

Found this in the dumpster today. (I like to dive in dumpsters) (diving in dumpsters is fun) 44000µF total capacitance. submitted by /u/LaurisNauris [link] [comments]

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NEC die encased. Not much more information was available for this IC. submitted by /u/USWCboy [link] [comments]

Independent power electronics company Quantum Power Transformation (QPT) of Cambridge, UK (which was founded in 2020) has won the ABB Power Density Start-up Challenge 2023 for Motor Drive Products. QPT says that its qGaN technology solves the issues of RF and overheating so that GaN can now be driven at the very high speeds needed to deliver major power savings in motors and drives...

KYOCERA SLD Laser Inc (KSLD) of Goleta, near Santa Barbara, CA, USA — which is commercializing gallium nitride (GaN)-based laser light sources for automotive, mobility, specialty lighting and consumer applications — has unveiled Laser-based Automotive Headlight Modules and FiberLight Grille Assemblies, demonstrating them at the Consumer Electronics Show (CES 2024) in Las Vegas (9–12 January)...

Silicon photonics-based chip-to-chip optical connectivity firm Ayar Labs of Santa Clara, CA, USA says that Ganesh Moorthy, president & CEO of Microchip Technology Inc, and technology entrepreneur Craig Barratt, former CEO of Atheros and current chair of the board of Intuitive Surgical, are joining its board of directors. Also, Ayar’s co-founder Vladimir Stojanovic has been named chief technology officer...

In a groundbreaking announcement, Intel Corporation reveals its comprehensive plan to infuse artificial intelligence (AI) into the automotive landscape, solidifying its commitment to the industry’s transformative shift towards electric vehicles (EVs). The company outlines its AI everywhere strategy and finalizes an acquisition deal with Silicon Mobility, a leading fabless silicon and software company specializing in Systems-on-Chips (SoCs) for intelligent electric vehicle energy management.

Key Developments:

• Acquisition of Silicon Mobility: Intel acquires Silicon Mobility, aligning with sustainability goals and addressing critical energy management needs in the EV sector.
• AI-Enhanced SoCs: Intel introduces a new family of AI-enhanced software-defined vehicle SoCs. Zeekr is the pioneering Original Equipment Manufacturer (OEM) to adopt these chips for advanced generative AI-driven in-vehicle experiences.
• Open UCIe-Based Chiplet Platform: Intel commits to deliver the industry’s first open Universal Chiplet Interface (UCIe)-based platform for Software-Defined Vehicles (SDVs) in collaboration with imec, ensuring rigorous quality and reliability for automotive applications.
• Industry-Defining Standards: Intel takes the lead in chairing a new international standard for EV power management, emphasizing the company’s role in steering the industry towards sustainable and efficient electric vehicles.

Intel’s Whole Vehicle Approach:

Jack Weast, Vice President and General Manager of Intel Automotive emphasizes the company’s holistic strategy, stating, “Intel is taking a ‘whole vehicle’ approach to solving the industry’s biggest challenges. Driving innovative AI solutions across the vehicle platform will help the industry navigate the transformation to EVs.”

AI-Enhanced SDV SoCs Unveiled:

The new family of AI-enhanced SoCs from Intel addresses the critical need for power and performance scalability. These chips draw from Intel’s AI PC roadmap, enabling advanced in-vehicle AI use cases like driver and passenger monitoring. A live demo showcased the simultaneous operation of 12 advanced workloads, highlighting the potential for consolidating legacy electronic control unit (ECU) architecture for improved efficiency and scalability.

Zeekr Takes the Lead:

Intel’s SDV SoCs will debut in Geely’s Zeekr brand, making it the first OEM to leverage Intel’s latest technology. The collaboration ensures forward compatibility on Intel systems, allowing Zeekr to scale and upgrade services to meet evolving customer demands for next-gen experiences.

Open Standards for a Sustainable Future:

Intel collaborates with SAE International to establish a committee delivering an automotive standard for Vehicle Platform Power Management (J3311). Inspired by proven power management techniques, the new standard aims to enhance energy efficiency and sustainability across all EVs. The committee includes industry representation from Stellantis, HERE, and Monolithic Power Systems, with openness for additional industry participation.

This strategic leap by Intel signifies a pivotal moment in the automotive industry, where cutting-edge AI technology meets the growing demand for sustainable and intelligent electric vehicles.

The post Intel Accelerates AI Integration in Automotive Industry with Strategic Moves and Open Standards appeared first on ELE Times.

Less than a year after Infineon snapped GAN Systems to bolster its gallium nitride (GaN) technology roadmap, another GaN semiconductor specialist is becoming part of a bigger semiconductor company’s product portfolio. Renesas is acquiring Transphorm to leverage its GaN expertise in power electronics, serving a wide range of segments like automotive, consumer, and industrial.

The acquisition deal, amounting to approximately $339 million, is expected to be complete by the second half of 2024. Subsequently, Renesas aims to incorporate Transphorm’s automotive-qualified GaN technology in its X-in-1 powertrain solutions for electric vehicles (EVs) besides computing, renewable energy, industrial, and consumer applications.

Renesas CEO Hidetoshi Shibata joins Transphorm co-founder, president and CEO Primit Parikh to announce the $339 million acquisition deal.

After establishing an in-house silicon carbide (SiC) production supported by a 10-year SiC wafer supply agreement, Renesas is now turning to its wide bandgap (WBG) cousin GaN to broaden energy-efficient and high-voltage component offerings. According to an industry study quoted in Renesas’ press release about the Transphorm acquisition, demand for GaN is predicted to grow by more than 50% annually.

Transphorm, co-founded by Umesh Mishra and Primit Parikh in 2007, has roots in technology developed at the University of California at Santa Barbara. It claims to be the first supplier of GaN semiconductors that are JEDEC- and automotive-qualified. Transphorm managers are also quick to point to another unique aspect of the company’s technology; unlike most GaN suppliers opting for e-mode, Transphorm has adopted the d-mode delivered by a cascade (normally off).

Transphorm recently made waves by claiming that it will unveil 1,200-V GaN semiconductors. Though the Goleta, California-based outfit has demonstrated 900-V GaN devices, it calls them merely a showpiece, reiterating its commitment to GaN-on-sapphire 1,200-V semiconductors initially targeted at e-bikes and e-scooters.

Another GaN supplier is gone, and a few more are left on the block. We are likely to see more of them gobbled by bigger chipmakers in a quest to grasp this next-generation material for power electronics. Starting from scratch doesn’t seem a viable option for large semiconductor outfits, especially in a technology that’s now in roller-coaster development mode while being in the midst of commercial realization.

So, what’s left on the GaN block? While Navitas Semiconductor, growing at an impressive pace, seems an unlikely acquisition target, there are a handful of smaller GaN outfits, including Cambridge GaN Devices, Efficient Power Conversion (EPC), QPT and VisIC Technologies. Pay heed to these GaN companies and their potential suitors in 2024.

Related Content

• SiC and GaN: A Tale of Two Semiconductors
• “Navitas Presents” to focus on GaN at CES 2023
• GaN’s applications roadmap spotted at CES 2023
• The diverging worlds of SiC and GaN semiconductors
• Integrated GaN Half-Bridge Delivers MHz Performance

The post Renesas’ Transphorm acquisition points to GaN writing on the wall appeared first on EDN.

Courtesy: Avnet

Anyone with a recently built car will probably have one or more buttons that they don’t use or don’t even know how to use. “Off-road” mode for an SUV used for the school run might be an example.

Another one in electric vehicles (EVs) that could rouse some curiosity soon is a button to enable bi-directional charging, or more correctly “bi-directional power transfer (BPT) in the on-board battery charger.” This is a feature of EVs that would make energy stored in the battery available for purposes other than traction. Another generic term is V2X or “Vehicle to (something)” power transfer.

Given widespread concerns about how long it takes to add range to an EV from the available charge points, it might seem odd that the ability to drain it away again might be seen as advantageous. There are reasons though why it can be a good thing, and we’ll look at these now.

Why do it?

The reason often given for BPT in EVs is that utility providers will give credit for energy returned to the grid to enable “peak shaving,” or providing an energy buffer for peak demands on the grid. Otherwise, utilities would have to bring extra power sources online at high cost to avoid outages. Timing is the issue here. EVs will often be “plugged in” overnight to charge, sometimes at a lower tariff, and this is when excess energy might be available. Peak demands are typically during the day when industry is operating. Of course, when EV adoption is ubiquitous, EVs will become a peak demand themselves as people return from work and plug in.

Those with solar power will be familiar with the principle of “feed-in” to the grid. But remember that after the capital expense, solar energy is effectively free so any credit is significant. Energy in EV batteries must be bought initially, so the benefit is the difference between debit and credit, which might be small or even negative. However, estimates show that a typical EV owner could save around $420 each year or $4,000 over the vehicle’s lifetime.

This hardly offsets the current extra cost of an EV over an internal combustion engine, but the owner might take the wider view to include environmental benefits and overall cost to society. With the increasing adoption of intermittent alternative energy sources such as solar and wind, EV batteries are seen as a valuable reserve to draw on by the utilities, making their argument for feed-in more compelling.

Another use for energy returned from the battery is V2H or “Vehicle to Home.” This could include groups of homes and even small businesses in a microgrid. Here, local alternative energy sources are combined with a grid connection and the energy storage in EVs to potentially provide independence from the grid for cost savings and resilience to power outages–if the EV is parked and connected. If not, then a wall-mount battery might be needed. Forward-looking property developers could include this in new builds. The capital cost to retrospectively install the infrastructure with the appropriate safety features is high.

A simpler “Vehicle to Load” arrangement might be a standard AC power socket on the EV, which could power hand tools outside or lighting when camping. Also, as battery-powered vehicles are adopted in the construction industry, the V2L AC power source will become an alternative to polluting and noisy diesel generators. The ability to provide power at emergency scenes from heavy-duty vehicles is also seen as a potential asset. This was done at the Fukushima nuclear plant disaster in March 2011.

Regulatory pressures

Whether the use cases described can be justified, authorities have taken the longer view and urged the automotive industry to include BPT in EVs. California, for example, got some way toward approving a bill in the state legislature to require all new EVs sold to include BPT. This was dropped after some opposition because of the extra cost expected to be added to an EV.

Other countries have initiatives such as the UK’s “Electric vehicle smart charging action plan” to help meet the UK’s 2050 climate change targets. Still, the plan identifies V2H as a challenge.

Major European auto manufacturers are including BPT in their roadmaps. In the U.S., GM announced that all its EVs will be bi-directional by 2026 and Tesla said its EVs will be by 2025. To support its introduction, BPT is now embedded in standard ISO 15118-20, which specifies the communications interface and charging infrastructure needed.

What’s available?

The first thing to note is that BPT is only applicable (at least now) to the vehicle onboard charger. Fast DC chargers are overwhelmingly situated at public stations and serve users who need to add range quickly to continue a journey. There are bi-directional DC chargers rolled out in trials, but most applications are, and will be, AC wall boxes, mostly Level 1 up to around 2kW and some Level 2 up to around 22kW. Note though that the return power is often pegged to a low rate, as low as 2.2kW for the MG ZS EV for example, and only up to 3.6kW for several makes, at about half of the battery charge rates.

An analysis of the online database of EVs by IEEE dated September 2023 identified just nine EVs available with bi-directional chargers, with a mix of DC and AC outputs.

Implementation of bi-directional power transfer

One of the reasons why BPT is promoted is because it’s a perceived added value that is relatively low cost to implement in a vehicle. The development of all the security, safety and control electronics and software is surely extremely complex, and this is why BPT-enabled EVs might initially be more expensive to recoup the costs, but the final hardware is a little different in the vehicle.

The reason for this is that on-board chargers have been continuously developed to be more efficient for the fastest charge time and smaller and lighter to not adversely affect range. This is in part achieved by replacing power diodes in various positions with MOSFETs, either silicon or silicon-carbide, which drop lower voltage and produce less dissipation. This saves energy and reduces heatsinking size, weight and costs. The MOSFETs can then also be configured to act as switches, reversing their function from rectification to power switching.

The outline principle is shown in the diagram. The circuit at the top is uni-directional, whereas the circuit at the bottom uses MOSFETs as synchronous rectifiers for bi-directional operation. The Totem Pole PFC stage with diodes now becomes an inverter and the symmetry of the isolated Dual Active Bridge DC-DC stage arrangement is seen, allowing power flow in either direction, depending on the control scheme.

The four MOSFETs in the primary of the DAB converter act as diodes in the reverse power flow direction, either by simply using their body diodes with the channels off, or for best efficiency, they are driven as synchronous rectifiers. The second circuit has become common anyway for best efficiency in uni-directional applications so is easily modified for reverse power flow simply by changing the gate drive control algorithms.

The traditional uni-directional charger (upper circuit) hasxl evolved to the high-efficiency version (lower circuit), which also enables bi-directional operation.

With the momentum of climate protection legislation behind it, bi-directional power transfer in EVs is a feature that will become common. Unlike some features in modern cars, the button that enables it could be one we eventually appreciate for its cost and environmental benefits.

The post Explore the challenges and opportunities of bi-directional charging and EVs appeared first on ELE Times.

Qualcomm executives reveal key trends in AI, consumer technology and more for the future.

Not that long ago, the banana-peel-and-beer-fueled DeLorean in “Back to the Future” was presented as comedy. Yet today, 10% of cars are electric-powered.1 Just a year ago, conversing with a computer in true natural language was science fiction, but we know now that the next generation will not know life without a personal AI assistant.

Generative AI was the undisputed game-changer across nearly every industry, and we will undoubtedly continue to feel its impact next year.

One of the reasons I love working at Qualcomm is that I am surrounded by inventors and business leaders who are developing and deploying the leading edge AI, high performance, low-power computing and connectivity technologies poised to deliver intelligent computing everywhere.

Exactly how generative AI and other technology trends will continue to play out next year, of course, no one can completely know. But as we close out 2023, I was interested in understanding what our executives here at Qualcomm thought would be the key trends of 2024. Here is what I heard.

1. AI PCs will drive a laptop replacement “super cycle”

The PC market is set to experience a transformative shift in 2024, fueled by a “super cycle” of laptop replacements with the convergence of AI advancements for PCs.

Morgan Stanley predicts a drastic shift, with 40% of laptops due for replacement in 2024, expected to rise to 65% by 2025.2

“We anticipate a market-defining “super cycle” in the PC starting in 2024, where the need for new laptops and the advancement of AI will drive a new era of PCs,” says Qualcomm Technologies’ Senior Vice President and GM of Compute and Gaming, Kedar Kondap, adding,

“This innovation is not just an evolution in the PC market, but a revolution, driving the demand for AI PCs forward and reshaping the computing experience for businesses and consumers into the new year.”

You only have to look at what Microsoft is doing to know that these Intelligent PC and AI assistants, like Copilot, are coming.

Unapologetic plug/reminder: In October, at Snapdragon Summit the likes of Microsoft, HP, Lenovo and Dell stood with us as we announced how we’re enabling the AI PC with Snapdragon X Elite, built to take on AI tasks.

A girl holds her smartphone that houses generative AI processing on device, instead of the cloud.

2. Generative AI will move from the cloud to personal devices

The generative AI conversation in 2023 was predominantly about the cloud, but privacy, latency and cost will increasingly be choke points that on-device AI capabilities can help solve.

“As generative AI becomes more integrated in our lives, our personal devices like our smartphones, PCs, vehicles, and even IoT devices will become the hubs for multi-modal generative AI models,” noted Qualcomm Technologies’ Senior Vice President & General Manager of Technology Planning & Edge Solutions, Durga Malladi.

Not only does it make sense to do many AI tasks on-device, but it also broadens the access of these awesome capabilities, for both the consumer and enterprises.

“This transition will usher in next-level privacy-focused, personalized AI experiences to consumers and enterprises, and cut down cloud costs for developers,” added Malladi. “With large generative AI multi-modal models running on devices, the shift from cloud-based to hybrid or on-device AI is inevitable.”

3. Your smartphone will become even more indispensable

As generative AI capabilities are brought onto the smartphone, personal AI assistants will evolve into indispensable companions, continuously learning from our daily lives to provide tailored experiences.

“Smartphones, our most personal devices, are poised to leverage multi-modal generative AI models and combine on-device sensor data,” said Qualcomm Technologies’ Senior Vice President & General Manager of Mobile Handset, Chris Patrick. He added,

“Your on-device AI assistant will evolve from generic responses to personalized, informative outcomes.”

Applications leveraging large language models (LLMs) and visual models will use sensor data such as health, location and hyperlocal information to deliver personalized, meaningful content.

Patrick added, “By using different modalities, these AI assistants will enable natural engagement and be able to process and generate text, voice, images and even videos, solely on-device. This will bring next-level user experience to the mainstream while addressing the escalating costs of cloud-based AI.”

Another unapologetic plug/reminder: Also at Snapdragon Summit, we demonstrated on-device personalization on our new Snapdragon 8 Gen 3 to enable this market need.

4. Creatives will get more creative

Deeper integration of AI in the creative and marketing process is inevitable.

“Generative AI is changing how we learn, how we play and how we work,” said Qualcomm Incorporated’s Chief Marketing Officer, Don McGuire, adding, “Not only is Qualcomm one of the largest companies enabling this technology, but as the CMO, I’m deploying the tools throughout the marketing organization.

“As a result, we’re seeing an increase in productivity level, time-to-market and efficiency, so the team can spend more time on strategy and creative collaboration, and less on time-consuming, repetitive tasks.

“It’s not about replacing people but augmenting and enhancing their capabilities.”

With access to vast amounts of data, generative AI can make suggestions and provide valuable insights. It enables marketers to target specific audiences more effectively and gives us the ability to produce highly personalized content across various mediums.

5. Consumers will push for open multi-device ecosystems

The adoption of open ecosystems will empower consumers with the freedom to select the best devices from a variety of brands that fit their specific needs.

This increased interoperability will drive innovation and enhance consumer experiences as brands compete on a level playing field, striving to outperform one another and deliver superior products.

“Consumers will be the driving force behind device makers opening their ecosystems, demanding enhanced communication and functionality across devices,”

says Qualcomm Technologies’ Senior Vice President & General Manager, Mobile, Compute & XR, Alex Katouzian.

“With the recent announcement of Apple’s rich communication services messaging integration, and technologies like Link to Windows and Snapdragon Seamless experiences becoming more widespread, there’s a growing push for interoperability across brands and platforms,” he adds. “This shift towards open ecosystems will empower consumers with greater choice, enabling them to select the best device for their specific needs.”

6. Mixed Reality will redefine your world

In 2024, mixed reality, virtual reality and extended reality (XR) will make their way into the mainstream as technologies once reserved for enthusiasts become integrated into consumer products.

Qualcomm Technologies’ Vice President and GM of XR Hugo Swart says,

“XR is entering a stage of rapid progress, thanks to the widespread adoption of mixed reality capabilities, smaller devices and the advancement of spatial computing.”

Affordable hardware options, such as Meta’s Quest 3 and Ray Ban Meta, are just the beginning of what’s to come.

Generative AI will play a crucial role in improving and scaling XR experiences, democratizing three-dimensional (3D) content generation through new tools and creating more realistic and engaging virtual environments.

Voice interfaces powered by generative AI will provide a natural and intuitive way to interact with XR devices, while personal assistants and lifelike 3D avatars, also powered by generative AI, will become increasingly prevalent in the XR space.

The post Generative AI in 2024: The 6 most important consumer tech trends for next year appeared first on ELE Times.

Courtesy: Brette Mullenaux | Microchip

This blog post explores how our secure Integrated Circuit (IC) devices play a critical role in ensuring the credibility of Direct Current (DC) metering applications.

Enhancing Trust in DC Metering Technology

Direct Current (DC) metering is essential in various industries including data centers, communications, transportation, industrial and renewable energy. The proliferation of DC circuits, especially in applications like Electric Vehicle (EV) charging stations, has led to an increased demand for reliable and trustworthy DC metering technology. Unlike AC metering, which may overlook losses that occur from AC-to-DC conversion, DC metering ensures the accurate measurement of energy consumption.

However, ensuring the credibility and integrity of DC metering data is a significant challenge. As global standards and regulations evolve to standardize metering results, the need for authentic and secure measurements becomes paramount. This is where our secure Integrated Circuit (IC) devices play a crucial role in ensuring the trustworthiness of DC metering applications.

The Importance of Reliable DC Metering

DC metering plays a critical role in applications such as DC fast charging Level 3 and above. In these scenarios, end users need to pay for the precise amount of energy they receive. AC metering may not provide accurate results due to the losses incurred during the AC-to-DC conversion process. Therefore, the use of DC metering is essential for billing transparency and fairness.

Global standards, like the German Eichrecht standard, are being developed to ensure that DC metering measurements are authentic and trustworthy. These standards require end users to have the means to validate the authenticity of energy measurements; this is where our secure IC devices come into play.

Challenges in Ensuring DC Metering Security

DC metering systems typically include a microcontroller (MCU) that is responsible for logic, LCD displays and communication protocols. While these systems often use MCUs from reputable suppliers, the security aspect is often implemented in the software. However, software-based security can expose DC meters to vulnerabilities that could compromise the credibility of the measurements.

Secure IC Devices: A Solution for Reliable DC Metering

We offer a wide range of solutions, including reference designs, that cater to vertical markets where DC metering is vital. One notable example is the market for EV chargers, where reliable measurements are crucial for accurate billing.

Our TA100, ATECC608 and ECC204 devices are specifically designed to address the security challenges in DC metering applications. These devices provide robust hardware-level protection for private keys and support ECC P256 ECDSA sign operations in hardware. By leveraging our CryptoAuthentication library, DC metering vendors can efficiently implement secure JSON-encrypted data signing.

OCMF: Ensuring Authenticity and Integrity

In the context of DC metering, the Open Charging Metering Format (OCMF) often comes into play, particularly in reference to the Eichrecht standard in Germany. OCMF is a JSON format that includes energy measurements and a valid ECC signature. This format allows end users to verify the authenticity of measurements by using the corresponding public key. Additionally, the German Eichrecht standard mandates that DC meters include a small display accessible to users for transparency and validation.

Our secure IC devices, including the TA100, ATECC608 and ECC204, ensure that private keys are securely stored in hardware, making it challenging for hackers to compromise the integrity of DC metering data. Implementing JSON data signing using these devices is straightforward thanks to the high-level APIs provided by our CryptoAuthentication library.

Conclusion

In an era where the credibility of DC metering data is crucial for various applications, our secure IC devices provide a robust solution for safeguarding private keys and ensuring the authenticity and integrity of measurements. By embracing hardware-level security, DC metering vendors can meet evolving standards and regulations while offering end users transparent and trustworthy billing. As DC metering continues to expand across different industries, our contribution to enhancing security in this field is invaluable.

The post Using Secure IC Devices to Maintain the Integrity of DC Current Metering Data appeared first on ELE Times.

Courtesy: Rahul Unnikrishnan Nair | Intel

The landscape of AI and natural language processing has dramatically shifted with the advent of Large Language Models (LLMs). This shift is characterized by advancements like Low-Rank Adaptation (LoRA) and its more advanced iteration, Quantized LoRA (QLoRA), which have transformed the fine-tuning process from a compute-intensive task into an efficient, scalable procedure.

Generated with Stable Diffusion XL using the prompt: “A cute laughing llama with big eyelashes, sitting on a beach with sunglasses reading in gibili style”

The Advent of LoRA: A Paradigm Shift in LLM Fine-Tuning

LoRA represents a significant advancement in the fine-tuning of LLMs. By introducing trainable adapter modules between the layers of a large pre-trained model, LoRA focuses on refining a smaller subset of model parameters. These adapters are low-rank matrices, significantly reducing the computational burden and preserving the valuable pre-trained knowledge embedded within LLMs. The key aspects of LoRA include:

• Low-Rank Matrix Structure: Shaped as (r x d), where ‘r’ is a small rank hyperparameter and ‘d’ is the hidden dimension size. This structure ensures fewer trainable parameters.
• Factorization: The adapter matrix is factorized into two smaller matrices, enhancing the model’s function adaptability with fewer parameters.
• Scalability and Adaptability: LoRA balances the model’s learning capacity and generalizability by scaling adapters with a parameter α and incorporating dropout for regularization.

Left: Integration of LoRA adapters into the model. Right: Deployment of LoRA adapters with a foundation model as a task-specific model library

Quantized LoRA (QLoRA): Efficient Finetuning on Intel Hardware

QLoRA advances LoRA by introducing weight quantization, further reducing memory usage. This approach enables the fine-tuning of large models, such as the 70B LLama2, on hardware like Intel’s Data Center GPU Max Series 1100 with 48 GB VRAM. QLoRA’s main features include:

• Memory Efficiency: Through weight quantization, QLoRA substantially reduces the model’s memory footprint, crucial for handling large LLMs.
• Precision in Training: QLoRA maintains high accuracy, crucial for the effectiveness of fine-tuned models.
• On-the-Fly Dequantization: It involves temporary dequantization of quantized weights for computations, focusing only on adapter gradients during training.

Fine-Tuning Process with QLoRA on Intel Hardware

The fine-tuning process starts with setting up the environment and installing necessary packages, including bigdl-llm for model loading, peft for LoRA adapters, Intel Extension for PyTorch for training using Intel dGPUs, transformers for finetuning and datasets for loading the dataset. We will walk through the high-level process of fine-tuning a large language model (LLM) to improve its capabilities. As an example, I am taking generating SQL queries from natural language input, but the focus is on general QLoRA finetuning here. For detailed explanations you can check out the full notebook that takes you from setting up the required python packages, loading the model, finetuning and inferencing the finetuned LLM to generate SQL from text on Intel Developer Cloud and also here.

Model Loading and Configuration for Fine-Tuning

The foundational model is loaded in a 4-bit format using bigdl-llm, significantly reducing memory usage. This step is crucial for fine-tuning large models like the 70B LLama2 on Intel hardware.

from bigdl.llm.transformers import AutoModelForCausalLM

# Loading the model in a 4-bit format for efficient memory usage

model = AutoModelForCausalLM.from_pretrained(

“model_id”,  # Replace with your model ID

load_in_low_bit=”nf4″,

optimize_model=False,

torch_dtype=torch.float16,

modules_to_not_convert=[“lm_head”],

)

Learning Rate and Stability in Training

Selecting an optimal learning rate is critical in QLoRA fine-tuning to balance training stability and convergence speed. This decision is vital for effective fine-tuning outcomes as a higher learning rate can lead to instabilities and the training loss to abnormally drop to zero after a few steps.

from transformers import TrainingArguments

# Configuration for training

training_args = TrainingArguments(

learning_rate=2e-5,  # Optimal starting point; adjust as needed

per_device_train_batch_size=4,

max_steps=200,

# Additional parameters…

)

During the fine-tuning process, there is a notable rapid decrease in the loss after just a few steps, which then gradually levels off, reaching a value near 0.6 at approximately 300 steps as seen in the graph below:

Text-to-SQL Conversion: Prompt Engineering

With the fine-tuned model, we can convert natural language queries into SQL commands, a vital capability in data analytics and business intelligence. To finetune the model, we must carefully convert the data into structured prompt like below as an instruction dataset with Input, Context and Response:

# Function to generate structured prompts for Text-to-SQL tasks

def generate_prompt_sql(input_question, context, output=””):

return f”””You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question and context regarding one or more tables.

You must output the SQL query that answers the question.

### Input:

{input_question}

### Context:

{context}

### Response:

{output}”””

Diverse Model Options

The notebook supports an array of models, each offering unique capabilities for different fine-tuning objectives:

NousResearch/Nous-Hermes-Llama-2-7b

NousResearch/Llama-2-7b-chat-hf

NousResearch/Llama-2-13b-hf

NousResearch/CodeLlama-7b-hf

Phind/Phind-CodeLlama-34B-v2

openlm-research/open_llama_3b_v2

openlm-research/open_llama_13b

HuggingFaceH4/zephyr-7b-beta

Enhanced Inference with QLoRA: A Comparative Approach

The true test of any fine-tuning process lies in its inference capabilities. In the case of the implementation, the inference stage not only demonstrates the model’s proficiency in task-specific applications but also allows for a comparative analysis between the base and the fine-tuned models. This comparison sheds light on the effectiveness of the LoRA adapters in enhancing the model’s performance for specific tasks.

Model Loading for Inference:

For inference, the model is loaded in a low-bit format, typically 4-bit, using bigdl-llm library. This approach drastically reduces the memory footprint, making it suitable to run multiple LLMs with high parameter count on a single resource-optimized hardware like Intel’s Data Center GPUs 1100. The following code snippet illustrates the model loading process for inference:

from bigdl.llm.transformers import AutoModelForCausalLM

# Loading the model for inference

model_for_inference = AutoModelForCausalLM.from_pretrained(

“finetuned_model_path”,  # Path to the fine-tuned model

load_in_4bit=True,  # 4 bit loading

optimize_model=True,

use_cache=True,

torch_dtype=torch.float16,

modules_to_not_convert=[“lm_head”],

)

Running Inference: Comparing Base vs Fine-Tuned Model

Once the model is loaded, we can perform inference to generate SQL queries from natural language inputs. This process can be conducted on both the base model and the fine-tuned model, allowing users to directly compare the outcomes and assess the improvements brought about by fine-tuning with QLoRA:

# Generating a SQL query from a text prompt

text_prompt = generate_sql_prompt(…)

# Base Model Inference

base_model_sql = base_model.generate(text_prompt)

print(“Base Model SQL:”, base_model_sql)

# Fine-Tuned Model Inference

finetuned_model_sql = finetuned_model.generate(text_prompt)

print(“Fine-Tuned Model SQL:”, finetuned_model_sql)

Following a 15-minute training session itself, the finetuned model demonstrates enhanced proficiency in generating SQL queries that more accurately reflect the given questions, compared to the base model. With additional training steps, we can anticipate further improvements in the model’s response accuracy:

Finetuned model SQL generation for a given question and context:

Base model SQL generation for a given question and context:

LoRA Adapters: A Library of Task-Specific Enhancements

One of the most compelling aspects of LoRA is its ability to act as a library of task-specific enhancements. These adapters can be fine-tuned for distinct tasks and then saved. Depending on the requirement, a specific adapter can be loaded and used with the base model, effectively switching the model’s capabilities to suit different tasks. This adaptability makes LoRA a highly versatile tool in the realm of LLM fine-tuning.

Checkout the notebook on Intel Developer Cloud

We invite AI practitioners and developers to explore the full notebook on the Intel Developer Cloud (IDC). IDC is the perfect environment to experiment with and explore the capabilities of fine-tuning LLMs using QLoRA on Intel hardware. Once you login to Intel Developer Cloud, go to the “Training Catalog” and under “Gen AI Essentials” in the catalog, you can find the LLM finetuning notebook.

Conclusion: QLoRA’s Impact and Future Prospects

QLoRA, especially when implemented on Intel’s advanced hardware, represents a significant leap in LLM fine-tuning. It opens up new avenues for leveraging massive models in various applications, making fine-tuning more accessible and efficient.

The post Text-to-SQL Generation Using Fine-tuned LLMs on Intel GPUs (XPUs) and QLoRA appeared first on ELE Times.