As formal verification becomes more common in the industry, design complexity continues to be a challenge. Article argues that this is a byproduct of design-centric approach (optimize area, power, speed) without considering verifiability. A verification-centric approach driven by polynomial formal verification analysis can produce verifiable designs.

Abstract: Recently, a lot of effort has been put into developing formal verification approaches by both academic and industrial research. In practice, these techniques often give satisfying results for some types of circuits, while they fail for others. A major challenge in this domain is that the verification techniques suffer from unpredictability in their performance. The only way to overcome this challenge is the calculation of bounds for the space and time complexities. If a verification method has polynomial space and time complexities, scalability can be guaranteed. In this tutorial paper, we review recent developments in formal verification techniques and give a comprehensive overview of Polynomial Formal Verification (PFV). In PFV, polynomial upper bounds for the run-time and memory needed during the entire verification task hold. Thus, correctness under resource constraints can be ensured. We discuss the importance and advantages of PFV in the design flow. Formal methods on the bit-level and the word-level, and their complexities when used to verify different types of circuits, like adders, multipliers, or ALUs are presented. The current status of this new research field and directions for future work are discussed.

cross-posted from: https://discuss.tchncs.de/post/8824219

One way to help alleviate the effects of the talent shortage is changing how semiconductors are designed so that organizations can achieve more with their existing workforce. This requires moving away from project-centric design and transitioning to an IP-centric design methodology.

Over the past few years, teams have moved from building relatively self-contained, isolated designs to creating complex platforms across dispersed and integrated design centers. Larger design footprints, a more comprehensive array of products and quicker time to market are other contributing factors to walking away from a project-based design methodology.

One way to help alleviate the effects of the talent shortage is changing how semiconductors are designed so that organizations can achieve more with their existing workforce. This requires moving away from project-centric design and transitioning to an IP-centric design methodology.

Over the past few years, teams have moved from building relatively self-contained, isolated designs to creating complex platforms across dispersed and integrated design centers. Larger design footprints, a more comprehensive array of products and quicker time to market are other contributing factors to walking away from a project-based design methodology.

For battery-operated devices, the energy consumption for chip production far exceeds the lifetime energy consumption of the chips themselves. So, if we want to save energy, we’d better focus on the manufacturing process, argues Bram Nauta.

cross-posted from: https://discuss.tchncs.de/post/4827653

So how can universities train students for a continuous and rapidly changing technology? This is especially difficult because it involves both software and hardware, and more domain-specific and increasingly heterogeneous architectures. And regardless of whether these devices are tethered to a battery or plugged into a socket, they need to be much more energy-efficient. Given the slowdown in Moore’s Law and the shrinking power, performance and area/cost benefits of scaling, that often requires a mix of computer science, electrical engineering, and in packages, an increasing amount of mechanical engineering.

“Mechanical engineers, electrical engineers, those disciplinary trainings through those curriculums, they’re accredited and we have a very vigorous process that will continue. But these smaller, bite-sized chunks of curriculum will allow a student to broaden. So as a mechanical engineer, I may not necessarily have either capacity in my studies, or the depth of interest, to take an entire course on heterogeneous integration. But I might be very open to a smaller, bite-sized piece that’s looking at the thermal properties of packaging and new effects occurring because of things like heterogeneous integration. And that is going to be very important for us to be more nimble, to get these things done more quickly.

“You could hire somebody who has a background in electrical engineering or computer engineering, where they understand the low-level hardware and how to build embedded systems and how to develop them, but they don’t usually have a background in securing them,” said Dan Walters, principal embedded security engineer and lead for microelectronics solutions at MITRE. “Or you could look at students with more of a focus in security and cybersecurity. Those typically are computer science degrees. And some universities have computer or cybersecurity degrees, but that’s really software-heavy. Those students don’t understand embedded systems and the unique things that come along with that. What we essentially did was hire from one of those two groups and say, ‘Okay, we’re going to do on-the-job training for the other 50% that you’re missing.'”

So how can universities train students for a continuous and rapidly changing technology? This is especially difficult because it involves both software and hardware, and more domain-specific and increasingly heterogeneous architectures. And regardless of whether these devices are tethered to a battery or plugged into a socket, they need to be much more energy-efficient. Given the slowdown in Moore’s Law and the shrinking power, performance and area/cost benefits of scaling, that often requires a mix of computer science, electrical engineering, and in packages, an increasing amount of mechanical engineering.

“Mechanical engineers, electrical engineers, those disciplinary trainings through those curriculums, they’re accredited and we have a very vigorous process that will continue. But these smaller, bite-sized chunks of curriculum will allow a student to broaden. So as a mechanical engineer, I may not necessarily have either capacity in my studies, or the depth of interest, to take an entire course on heterogeneous integration. But I might be very open to a smaller, bite-sized piece that’s looking at the thermal properties of packaging and new effects occurring because of things like heterogeneous integration. And that is going to be very important for us to be more nimble, to get these things done more quickly.

“You could hire somebody who has a background in electrical engineering or computer engineering, where they understand the low-level hardware and how to build embedded systems and how to develop them, but they don’t usually have a background in securing them,” said Dan Walters, principal embedded security engineer and lead for microelectronics solutions at MITRE. “Or you could look at students with more of a focus in security and cybersecurity. Those typically are computer science degrees. And some universities have computer or cybersecurity degrees, but that’s really software-heavy. Those students don’t understand embedded systems and the unique things that come along with that. What we essentially did was hire from one of those two groups and say, ‘Okay, we’re going to do on-the-job training for the other 50% that you’re missing.'”

Exascale is the next frontier in computing power, where systems are built to carry out extremely complex functions with increased speed and precision. This in turn enables researchers to accelerate their work into some of the most pressing challenges we face, including the development of new drugs, and advances in nuclear fusion to produce potentially limitless clean low-carbon energy.

The exascale system hosted at the University of Edinburgh will be able to carry out these complicated workloads while also supporting critical research into AI safety and development, as the UK seeks to safely harness its potential to improve lives across the country.

I sleep with my wife and (when she graces us with her presence) our cat. Last night I caught myself syncing my breath to their breaths while sleeping, or half-sleeping considering I was aware of what was happening. Eventually their breathing went out of sync, and my breathing got confused, and after a very brief period of suffocation, I realized that I have no obligation to sync my breath, and took control of my breathing and started breathing normally. It felt strange to me but I googled it and it looks like syncing your breath happens to people. Does it happen to you as well?

PS: I realized while typing, I don't know if I should be hearing my 3kg cat's breathing. I should check on that.

cross-posted from: https://discuss.tchncs.de/post/3979328

Engineers in Princeton managed to train GPT4 and extend AutoSVA to generate SVA (systemverilog assertions) from buggy RTL and functionality description. SVA is widely used to verify digital design for ASIC and FPGAs. AutoSVA2, which extends open-source AutoSVA, improves the flow to generate SVA from English description. LLM was trained in multiple iterations to generate SVA with correct syntax, which is something GPT fails to do by itself. Authors argue that GPT's "creativity" allows it to write correct assertion even from a buggy RTL. Later authors used this tool to write RTL from scratch as well. RTL written by GPT was tested against the SVA generated by this tool, and SVA corrected by an engineer was fed back to LLM, which generated functionally correct FIFO queue in a few iterations.

Abstract—Formal property verification (FPV) has existed for decades and has been shown to be effective at finding intricate RTL bugs. However, formal properties, such as those written as SystemVerilog Assertions (SVA), are time-consuming and error- prone to write, even for experienced users. Prior work has attempted to lighten this burden by raising the abstraction level so that SVA is generated from high-level specifications. However, this does not eliminate the manual effort of reasoning and writing about the detailed hardware behavior. Motivated by the increased need for FPV in the era of heterogeneous hardware and the advances in large language models (LLMs), we set out to explore whether LLMs can capture RTL behavior and generate correct SVA properties. First, we design an FPV-based evaluation framework that measures the correctness and completeness of SVA. Then, we evaluate GPT4 iteratively to craft the set of syntax and semantic rules needed to prompt it toward creating better SVA. We extend the open-source AutoSVA framework by integrating our improved GPT4-based flow to generate safety properties, in addition to facilitating their existing flow for liveness properties. Lastly, our use cases evaluate (1) the FPV coverage of GPT4-generated SVA on complex open-source RTL and (2) using generated SVA to prompt GPT4 to create RTL from scratch. Through these experiments, we find that GPT4 can generate correct SVA even for flawed RTL—without mirroring design errors. Particularly, it generated SVA that exposed a bug in the RISC-V CVA6 core that eluded the prior work’s evaluation.

Engineers in Princeton managed to train GPT4 and extend AutoSVA to generate SVA (systemverilog assertions) from buggy RTL and functionality description. SVA is widely used to verify digital design for ASIC and FPGAs. AutoSVA2, which extends open-source AutoSVA, improves the flow to generate SVA from English description. LLM was trained in multiple iterations to generate SVA with correct syntax, which is something GPT fails to do by itself. Authors argue that GPT's "creativity" allows it to write correct assertion even from a buggy RTL. Later authors used this tool to write RTL from scratch as well. RTL written by GPT was tested against the SVA generated by this tool, and SVA corrected by an engineer was fed back to LLM, which generated functionally correct FIFO queue in a few iterations.

Abstract—Formal property verification (FPV) has existed for decades and has been shown to be effective at finding intricate RTL bugs. However, formal properties, such as those written as SystemVerilog Assertions (SVA), are time-consuming and error- prone to write, even for experienced users. Prior work has attempted to lighten this burden by raising the abstraction level so that SVA is generated from high-level specifications. However, this does not eliminate the manual effort of reasoning and writing about the detailed hardware behavior. Motivated by the increased need for FPV in the era of heterogeneous hardware and the advances in large language models (LLMs), we set out to explore whether LLMs can capture RTL behavior and generate correct SVA properties. First, we design an FPV-based evaluation framework that measures the correctness and completeness of SVA. Then, we evaluate GPT4 iteratively to craft the set of syntax and semantic rules needed to prompt it toward creating better SVA. We extend the open-source AutoSVA framework by integrating our improved GPT4-based flow to generate safety properties, in addition to facilitating their existing flow for liveness properties. Lastly, our use cases evaluate (1) the FPV coverage of GPT4-generated SVA on complex open-source RTL and (2) using generated SVA to prompt GPT4 to create RTL from scratch. Through these experiments, we find that GPT4 can generate correct SVA even for flawed RTL—without mirroring design errors. Particularly, it generated SVA that exposed a bug in the RISC-V CVA6 core that eluded the prior work’s evaluation.

One of the biggest shortcomings of silicon is that it can only be made so thin because its material properties are fundamentally limited to three dimensions [3D]. For this reason, two-dimensional [2D] semiconductors—so thin as to have almost no height—have become an object of interest to scientists, engineers and microelectronics manufacturers.

Thinner chip components would provide greater control and precision over the flow of electricity in a device, while lowering the amount of energy required to power it. A 2D semiconductor would also contribute to keeping the surface area of a chip to a minimum, lying in a thin film atop a supporting silicon device.

But until recently, attempts to create such a material have been unsuccessful.

Now, researchers at the University of Pennsylvania School of Engineering and Applied Science have grown a high-performing 2D semiconductor to a full-size, industrial-scale wafer. In addition, the semiconductor material, indium selenide (InSe), can be deposited at temperatures low enough to integrate with a silicon chip.

"For the purposes of an advanced computing technology, the chemical structure of 2D InSe needs to be exactly 50:50 between the two elements. The resulting material needs a uniform chemical structure over a large area to work," says Song.

The team achieved this groundbreaking purity using a growth technique called "vertical metal-organic chemical vapor deposition" (MOCVD). Previous research had attempted to introduce the indium and selenium in equal quantities and at the same time. Song demonstrated, however, that this method was the source of undesirable chemical structures in the material, producing molecules with varying ratios of each element. MOCVD, by contrast, works by sending the indium in a continuous stream while introducing the selenium in pulses.