Limiting AI/ML Tools To Ensure Physical AI Safety, Security


Key Takeaways: AI-based tools can help monitor physical AI systems and LLMs, but human oversight is still needed to avoid false positives, bias, and other anomalies. For autonomous vehicles and robots, edge case scenarios and understanding human values are weak points, especially as moral and social values change over time. AI tools are growing and becoming increasingly helpful for c... » read more

Using Data And AI More Effectively In EDA


Key Takeaways The data being produced by EDA tools tends to be for human consumption and has weak semantics. Agents are attempting to create actionable information from unstructured data. The Model Context Protocol may provide AI with access to better data. Semiconductor design generates a lot of data, but how much of that is useful or currently being used by AI tools? And h... » read more

AI Starting To Simplify Design Of Programmable Logic


Key Takeaways AI/ML and agentic tools are getting better at helping design and compile FPGAs, but downstream programming is slower to benefit. FPGAs historically have been designed using Verilog or VHDL, but higher-level languages could push more intelligence into compilers. ML tools can also help with mixed-signal co-design by automatically tuning DSP algorithms based on analog simu... » read more

Chip Industry Week in Review


The IEEE ISSCC conference was held this week in San Francisco. Among the highlights: IBM detailed an AI accelerator based on its new inferencing dataflow architecture. CEA-Leti presented a chip-scale, ultra-fast, battery-operated EPR spectrometer. QuTech introduced a cryo-CMOS SoC with NV centers in diamond. UTokyo showed its low-jitter PLL architecture for beyond 5G/6G. Imec d... » read more

Can A Computer Science Student Be Taught To Design Hardware?


Key Takeaways New approaches are being devised and tested to address the talent shortage. Leveraging AI in design tools will help engineers become more efficient, and potentially could reduce the time it takes to train engineering students. EDA companies are looking at whether it's possible to train computer science and software engineers to become hardware engineers. A vari... » read more

The Race Begins For Much Bigger Abstractions In Data Centers


Key Takeaways Data center build-out is enabling much larger and more complex abstractions. Competition is building for digital/virtual twins across multiple industry segments, including automotive, aerospace, and chip manufacturing. AI, and particularly AI agents, will play a significant role in sorting through data to find potential trouble spots. The frenzy of new data cen... » read more

Minimum Energy Per Query


Key Takeaways Extracting heat from a chip faster is a short-term fix to a bigger problem. The longer-term challenge is how to reduce the amount of energy used per query. Data movement, guardbanding, and software inefficiency are key targets for the future. Heat is a serious problem within AI chips, and it is limiting how much processing can be done. The solution is either to... » read more

Does Your RISC-V Core Meet The Standard?


Key Takeaways Architectural conformance and implementation verification are necessary but different for RISC-V designs, yet few verification engineers have experience on the conformance side. While RISC-V enables flexibility, there is a potential for ecosystem fragmentation. It is mathematically impossible to test every instruction combination, so engineers are moving beyond just "bl... » read more

Multi-Die Assemblies Require More Detailed Test Plan Earlier


Key Takeaways Design for test takes on new urgency in complex multi-die assemblies, where it can be used to minimize downstream errors and the cost of fixing them. DFT needs to be increasingly detailed due to more connections and the inability to access some components. DFT strategies need to be developed earlier and may require multiple testing approaches. Multi-die assembl... » read more

AI’s Impact On Engineering Jobs May Be Different Than Expected


Key Takeaways: AI is expected to eliminate many repetitive, entry-level tasks, but that may allow engineering students trained on the latest tools to start in more senior positions. AI is a force multiplier. It can accelerate the learning curve for junior engineers. While AI is very good at solving multi-dimensional problems, domain expertise, critical thinking, and sanity checks wil... » read more

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