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AI-powered tool streamlines software development for greater efficiency and accuracy

Microchip Technology is leveraging the power of Artificial Intelligence (AI) to assist software developers and embedded engineers in writing and debugging code with the launch of its MPLAB AI Coding Assistant. A Microsoft Visual Studio Code (VS Code) extension, the free tool is based on Continue—the market’s leading open-source AI code assistant—and comes preconfigured with Microchip’s AI chatbot for real-time support.

The Microchip chatbot enables a chat functionality which allows developers to evaluate and iterate on code directly from the sidebar. This interactive support enhances the coding experience by providing highly customized, relevant real-time assistance and insights on Microchip-specific products. Additional features include advanced autocomplete for easier coding, an edit feature and error detection for efficient code modifications within the current file and integrated access to searchable Microchip documentation within the IDE.

“The MPLAB AI Coding Assistant represents a significant leap forward in software development and will transform how engineers work with Microchip products,” said Rodger Richey, vice president of development systems and academic programs at Microchip. “We’re harnessing the power of AI to provide interactive, real-time support that helps developers create better software, more quickly and with less hassle.”

Unlike most other code assistants on the market, MPLAB AI Coding Assistant’s sidebar chat feature can deliver block diagrams directly within the VS Code interface rather than just text responses. This capability, combined with easy access to a library of documentation on Microchip microcontrollers and microprocessors, streamlines the coding process and helps enhance accuracy.

Visit the website to learn more about Microchip’s wide range of development tools.

Pricing and Availability

The MPLAB AI Coding Assistant is available for free; some advanced features may require a subscription license. For additional information contact a Microchip sales representative, authorized worldwide distributor or visit Microchip’s Purchasing and Client Services website, www.microchipdirect.com.

The post Artificial Intelligence Meets Embedded Development with Microchip’s MPLAB AI Coding Assistant appeared first on ELE Times.

Photos 1, 2: Ч1-40 (Ch1-40) DOCXO quartz frequency standard. Photos 3-6: В7-34А (V7-34A) Digital voltmeter. 5.5 digits. Features ovenized voltage reference, fully isolated and hermetically sealed analog part. Photo 7: С1-107 (S1-107) Hybrid portable oscilloscope/multimeter with multimeter part drawn directly on the scope tube. submitted by /u/AltCtrlGraphene [link] [comments]

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Infinera Corp of San Jose, CA, USA — a vertically integrated manufacturer of open optical networking systems and optical semiconductors — says that its pending acquisition by Nokia Corp should be completed on or about 28 February, subject to receipt of remaining outstanding regulatory approvals and the satisfaction of other remaining customary closing conditions...

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Rheostats

Rheostats are simple and ubiquitous circuit elements, usually comprising a potentiometer connected as an adjustable two terminal resistor. The availability of manual pots with resistances spanning ohms to megohms makes the optimum choice of nominal resistance easy. But when an application calls for a digital potentiometer (Dpot), the problem can be challenging.

Wow the engineering world with your unique design: Design Ideas Submission Guide

Dpots are only available in a resistance range that’s narrow compared to manual pots. They also typically suffer from problematically high wiper resistance and resistance tolerance. These limitations conspire to make Dpots a difficult medium for implementing precision rheostats. Recent EDN design idea (DI) articles have addressed these issues with a variety of strategies and topologies:

• Op-amp wipes out DPOT wiper resistance
• Synthesize precision Dpot resistances that aren’t in the catalog
• Synthesize precision bipolar Dpot rheostats
• A class of programmable rheostats

While each of these designs corrects one or more complaints on the lengthy list of digital rheostat shortcomings, none fixes them all and some introduce complications of their own. Examples include crossover distortion, unreduced sensitivity to resistance tolerances, resolution-reducing nonlinearity of the programmed resistance, and just plain old complexity.

The design

Figure 1’s circuit isn’t a perfect solution either. But it does synthesize an accurate programmed resistance equal to reference resistor R1 linearly multiplied by U1’s Rbw/Rab digital setting (the ratio between the terminal B to wiper resistance and total element resistance).

Figure 1 A precision digital rheostat that synthesizes an accurate programmed resistance equal to reference resistor R1 linearly multiplied by U1’s Rbw/Rab.

Here’s how it works.

 R = (Va – Vb)/Ia
R = R1/(Raw/Rbw + 1) = R1 Rbw/Rab
Rab = Raw + Rbw = typically 5k to 10k

Where R is the programmed synthetic resistance, R1 is the reference resistor, Raw is the resistance between terminal A and wiper terminal, Rbw is the resistance between B and wiper terminal, and Rab is the total element resistance.

U1 works in “voltage divider” (pot) mode to set the gain of inverting amplifier A2. Pot mode makes gain insensitive to both U1’s wiper resistance (Rw) and Rab. They really don’t matter much—see Figure 4-4 in the Microchip MCP41XXX/42XXX datasheet.

Turning the crank on Figure 1’s design equation math, we get:

Ga2 = Raw/Rbw

Where Ga2 is A2’s gain. Further,

Voltage across R1 = (Va – Vb) + Ga2(Va – Vb) = (Raw/Rbw + 1)(Va – Vb) =  Rab/Rbw(Va – Vb)
Current through R1 = Ia = Rab/Rbw(Va – Vb)/R1

Then, since R = (Va – Vb)/Ia:

R = R1*Rbw/Rab

Va is lightly loaded by A1’s ~10 picoamp (pA) input bias, so R1 can range from hundreds of ohms up to multiple megohms as the application may dictate. It should be precision, certainly 1% or better; then, programming and the math above takes over.

Figure 2 plots the linear relationship between R and Rbw.

Figure 2 Linear relationship between R and Rbw showing the circuit synthesizes an accurate programmed resistance equal to reference resistor R1 linearly multiplied by U1’s Rbw/Rab.

A compensation capacitor (C1) probably isn’t necessary for the parts selection shown in Figure 1 for A2 and U1. But if a faster amplifier or a higher resistance Dpot is chosen, then 10 pF to 20 pF would probably be prudent.

Meanwhile, I think it would be fair to say this design looks competitive with its peers. But earlier I described it as imperfect. Besides being a single-terminal topology (like two others on the list), where else does it fall short of being a complete solution to the ideal digital rheostat (Digistat) problem?

Shortcomings

Here’s where: As Figure 3 shows, when the programmed value for R goes down, A2’s gain (Ga2) must go up. Reading the graph from right to left, we see gain rising moderately as R declines by 75% from R1 to R1/4 where, Rbw/Rab = 64/256 and gain = 3, but then it takes off. This tends to exaggerate errors like input offset, finite GBW and other op-amp nonidealities while creating the possibility of early A2 saturation at relatively low signal levels.

Figure 3 Graphs for Ga2 (red) and R/R1 (black) versus Rbw/Rab on the x-axis. When the programmed value for R goes down, Ga2 must go up.

The severity of the impact of these effects on utility of the design, whether mild, serious, or fatal, will depend on how low you need go in R/R1 and other specifics of the application. So, it’s certainly not perfect, but maybe it’s still useful somewhere.

Two-terminal design

And about that single terminal problem. If you have an application that absolutely requires a two-terminal programmable resistance, you might consider Figure 4. Depending on the external circuitry, it might not oscillate.

Figure 4 Duplicate and cross-connect Figure 1’s circuitry to get a two-terminal programmable resistance.

In closing…

Thanks to frequent contributor Christopher R. Paul for his clever innovations and stimulating discussions on this topic, I would likely never have come up with this design without his help. More thanks go to editor Aalyia Shaukat for her clever creation of this DI section that makes fun teamwork like this possible in the first place. This article would definitely never have happened without her help.

Stephen Woodward’s relationship with EDN’s DI column goes back quite a long way. Over 100 submissions have been accepted since his first contribution back in 1974.

 Related Content

• Op-amp wipes out DPOT wiper resistance
• Synthesize precision Dpot resistances that aren’t in the catalog
• Synthesize precision bipolar Dpot rheostats
• A class of programmable rheostats

The post A precision digital rheostat appeared first on EDN.

Fabless firm Cambridge GaN Devices Ltd (CGD) — which was spun out of the University of Cambridge in 2016 to design, develop and commercialize power transistors and ICs that use GaN-on-silicon substrates — has closed a $32m Series C funding round led by a strategic investor with participation from British Patient Capital and supported by existing investors Parkwalk, BGF, Cambridge Innovation Capital (CIC), Foresight Group, and IQ Capital...

A new network-on-chip (NoC) IP aims to dramatically accelerate chip development by introducing artificial intelligence (AI)-driven automation and reducing wire length to lower power use in system-on-chip (SoC) interconnect design. Arteris, which calls its newly introduced FlexGen interconnect IP a smart NoC, claims to deliver a 10x productivity boost, shortening design iterations from weeks to days.

Modern chips—connected by billions of wires—are ever-expanding with growing complexity. Modern SoCs have 5 to 20+ unique NoC instances, and each instance can require 5-10 iterations. As a result, SoC design complexity has surpassed manual human capabilities, which calls for smarter NoC automation.

“In SoC interconnect, while technology has advanced to new levels, a lot of work is still done in manual mode,” said Michal Siwinski, CMO of Arteris. FlexGen accelerates chip design by shortening and reducing iterations from weeks to days for greater efficiency.

“While FlexGen is still using the tried-and-tested NoC IP technology as basic building blocks, it automates the existing infrastructure by employing AI technology,” said Andy Nightingale, VP of product management and marketing at Arteris. “With FlexGen, we automate the NoC IP generation to reduce the manual work while opening high-quality configurations that rival or surpass the manual designs.”

Figure 1 A FlexNoC manual interconnect (above) is shown for an ADAS chip, while an automated FlexGen interconnect (blow) accelerates this chip design by up to 10x. Source: Arteris

According to Nightingale, it enhances engineering efficiency by 3x while delivering expert-quality results with optimized routing and reduced congestion. Dream Chip Technologies, a supplier of advanced driver assistance systems (ADAS) silicon solutions, acknowledges reducing design iterations from weeks to days while using FlexGen in its Zukimo 1.1 automotive ADAS chip design.

“FlexGen’s automated NoC IP generation allows us to create floorplan adaptive topologies with complex automotive traffic requirements within minutes,” said Jens Benndorf, GM at Dream Chip Technologies. “That enabled rapid experimentation to find design sweet spots and to respond quickly to floorplan changes with almost push-button timing closure.”

Shorter wire length

With AI comes a compute performance explosion, and as a result, the complexity of interconnects is going to exponential levels in SoC designs, leading to a huge explosion in the number of wires. FlexGen claims to reduce wire length by up to 30% to improve chip or chiplet power efficiency.

“We are also tackling the big problem of wire length in modern SoC designs,” said Nightingale. “As the gate count size reduces, it inevitably leads to dynamic power issues due to massive data traffic across wires.” By reducing wire length, FlexGen interconnect IP can reduce overall system power and thus help heating problems caused by the energy density of moving massive amounts of data across SoC interconnects.

Figure 2 FlexNoC manual interconnect (above) is shown with the best performance, while automated FlexGen (below) significantly reduces the interconnect wire length. Source: Arteris

Siwinski added that the number of gates doesn’t matter at smaller nodes. “Power from wire length kills you, so we reduce wire length to reduce overall power, performance, and area (PPA) in SoC designs.” That’s crucial as SoCs scale and become more powerful to meet the demands of applications like AI, autonomous driving, and cloud computing.

FlexGen is processor agnostic and supports Arm, RISC-V, and x86 processors. Moreover, its IP generation is highly repeatable to facilitate incremental design.

Related Content

• SoC Interconnect: Don’t DIY!
• What is the future for Network-on-Chip?
• Why verification matters in network-on-chip (NoC) design
• SoC design: When is a network-on-chip (NoC) not enough
• Network-on-chip (NoC) interconnect topologies explained

The post SoC interconnect automates processes, reduces wire length appeared first on EDN.