How AI Will Automate Chip Design


AI has been used in EDA for many years for the core algorithms in tools, but it's getting smarter and more optimized with the rollout of generative and agentic AI. As it evolves and improves, hardware engineers are finding ways to leverage it for more complex tasks. Ziyad Hanna, corporate vice president at Cadence, talks about five levels of autonomy in chip design that mirror those in the auto... » read more

HBM4E Raises The Bar For AI Memory Bandwidth


The pace of AI innovation continues to expose a painful reality. Compute keeps scaling, but memory bandwidth remains one of the hardest bottlenecks to remove. As AI models grow larger and more complex, feeding data fast enough into accelerators has become just as critical as raw compute capability. High Bandwidth Memory (HBM) has been central to solving this challenge, and the next step in that... » read more

Human-Centered Agentic AI Comes To RTL Verification


For decades, productivity gains in electronic design automation (EDA) came from better engines. Faster solvers, higher-capacity simulators, and more scalable formal tools allowed design and verification teams to keep pace as designs grew larger. That model is no longer sufficient. Today’s design and verification bottleneck is not raw tool performance, but the coordination overhead required... » read more

Rethinking Voice AI At The Edge: A Practical Offline Pipeline


Cloud-based AI dominates the headlines, but responsive and private interaction lies at the edge. This blog post shows how to build a fully offline, real-time voice assistant using the Arm-based NVIDIA DGX Spark platform. The system integrates open-source components such as faster-whisper and vLLM. It delivers low-latency, human-like dialogue without sending data outside the local environment. ... » read more

AI Power on the Edge


Key takeaways Power and thermal become primary design considerations, not just optimizations. Hardware architectures need to be developed from the ground up. Hardware/software/model co-development is essential. Implementing AI on the edge is driven by a different set of metrics than training or even inference in the cloud. It makes power a first-class citizen, if not the mos... » read more

The Petabyte Problem: How AI Is Finally Making Semiconductor Manufacturing Data Actionable


The semiconductor industry has quietly accumulated one of the most complex data challenges in modern manufacturing, and it has largely been losing the battle to solve it. Modern fabs now generate gigabytes of data per chip across probe, assembly, and test operations. Test programs routinely exceed one million test items. The largest enterprise deployments have crossed the multi-petabyte thre... » read more

Ensuring AI Reliability: Mitigating OCP’s Silent Data Corruption Risks


Silent Data Corruption (SDC) is an industry challenge affecting data centers worldwide with increasing frequency. This phenomenon stems from untraceable hardware failures that make detection notoriously difficult. SDCs don’t leave any record in system logs or trigger exception mechanisms. The corrupted data they produce can propagate unnoticed, causing cascading failures that often demand ext... » read more

How AI Is Changing Computing And Why Testing Is Critical


Artificial intelligence (AI) is transforming industries, enhancing our daily lives, and improving efficiency and decision-making, but its need for compute processing power is growing at an astonishing rate, doubling every three months (Figure 1). To maintain this pace, the semiconductor industry is moving beyond traditional chip development – it has entered the era of heterogeneous chiplets i... » read more

Ultra Ethernet Security (UET‑TSS) Tailored For AI And HPC


As AI and high‑performance computing (HPC) systems scale from racks to entire data centers, the network has become both a performance enabler and a growing attack surface. Modern AI fabrics interconnect thousands of GPUs and CPUs, move massive volumes of sensitive model data, and increasingly rely on direct memory access rather than host‑mediated communication. These trends exposed a fundam... » read more

25G Ethernet: Scaling Data Movement For ADAS, Industry 4.0, And 5G Systems


The automotive and industrial markets are undergoing rapid transformation, driven by Advanced Driver Assistance Systems (ADAS) adoption, Industry 4.0 automation, and the rollout of 5G infrastructure. These trends are driving an unprecedented demand for edge AI capabilities and connectivity, with the global Edge AI IC market projected to grow at a 34.7% CAGR and reach $340B by 2034 [1]. Traditio... » read more

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