tag: ChipAgents

Data Boom Puts Pressure On NoCs, Fabrics
By Liz Allan - 26 Mar, 2026 - Comments: 0

Key Takeaways: NoC challenges, such as wiring congestion, timing closure, and performance, must be considered in tandem with topology and placement. Topologies can be customized to meet an application’s specific data flow needs, with a system containing multiple topologies to suit different data or zones. What is challenging for one type of system, such as an SoC, switch, or AI chi... » read more

AI Won’t Kill Verification IP, But It Will Redefine It
By Ann Mutschler - 26 Mar, 2026 - Comments: 0

Key Takeaways AI will enhance, not replace, verification IP by automating test generation and debug. Verification IP’s core value will increasingly lie in trust, accountability, and system-level realism, especially as designs become more complex, multi-die, and security-sensitive. AI shifts verification bottlenecks from execution to specification quality, raising expectations for c... » read more

AI Design Reshapes Data Management
By Ann Mutschler - 16 Mar, 2026 - Comments: 0

Key takeaways: Integrating AI into chip workflows is pushing companies to overhaul their data management strategies, shifting from passive storage to active, structured, and machine-readable systems. As training and inference workloads grow, data movement, congestion, and energy efficiency become the dominant challenges, often surpassing raw compute capability. Proprietary and comple... » read more

Limiting AI/ML Tools To Ensure Physical AI Safety, Security
By Liz Allan - 05 Mar, 2026 - Comments: 0

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
By Brian Bailey - 26 Feb, 2026 - Comments: 0

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
By Liz Allan - 26 Feb, 2026 - Comments: 0

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
By The SE Staff - 20 Feb, 2026 - Comments: 0

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?
By Liz Allan - 17 Feb, 2026 - Comments: 2

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
By Ed Sperling - 17 Feb, 2026 - Comments: 0

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
By Brian Bailey - 12 Feb, 2026 - Comments: 1

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

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