The competitive landscape of hardware-assisted verification (HAV) has evolved dramatically over the past decade. The strategic drivers that once defined the market have shifted in step with the rapidly changing dynamics of semiconductor design. Design complexity has soared, with modern SoCs now integrating tens of billions of transistors, multiple dies, and an ever-expanding mix of … Read more
VSORA, a pioneering high-tech company, has engineered a novel architecture designed specifically to meet the stringent demands of AI inference—both in datacenters and at the edge. With near-theoretical performance in latency, throughput, and energy efficiency, VSORA’s architecture breaks away from legacy designs optimized for training workloads. The team behind VSORA has deep roots in the … Read more
The exponential growth of AI models is hitting a memory wall, and Vsora’s Jotunn8 processor has been designed to solve this fundamental bottleneck. Paris-based fabless semiconductor company Vsora announced it has completed the tapeout of its Jotunn8 AI inference chip, marking the transition from design to fabrication. The chip, manufactured using TSMC’s 5-nm process and … Read more
Recently, I had the opportunity to speak with Synopsys’ distinguished experts in speed adapters and in-circuit emulation (ICE). Many who know my professional background see me as an advocate for virtual, transactor-based emulation, hence I was genuinely surprised to discover the impressive results achieved by today’s speed adapters critical to the validation of system in … Read more
In the rapidly evolving domain of AI processors, performance benchmarking has become essential for distinguishing meaningful innovation from hype. The semiconductor industry is in the midst of rethinking time-honored verification strategies, a result of increasingly layered software stacks in today’s system-on-chip (SoC) designs. The answer is often real-world, application software workloads, frequently from standardized benchmarks … Read more
Over the past several years, the lion’s share of artificial intelligence (AI) investment has poured into training infrastructure—massive clusters designed to crunch through oceans of data, where speed and energy efficiency take a back seat to sheer computational scale. Training systems can afford to be slow and power-hungry; if it takes an extra day or … Read more
Introduction: The Historical Roots of Hardware-Assisted Verification The relentless pace of semiconductor innovation continues to follow an unstoppable trend: the exponential growth of transistor density within a given silicon area. This abundance of available semiconductor fabric has fueled the creativity of design teams, enabling exponentially advanced systems-on-chip (SoCs). Yet, the very scale that empowers new … Read more
My head is currently spinning like a top. I foolishly wondered how much power AI-heavy data centers are currently consuming, and how much they are expected to consume in the coming years, and now I’m sorry I asked. The International Energy Agency (IEA) forecasts that global electricity demand from data centers will more than double … Read more
Inference is rapidly emerging as the next major frontier in artificial intelligence (AI). Historically, the AI development and deployment focus has been overwhelmingly on training with approximately 80% of compute resources dedicated to it and only 20% to inference. That balance is shifting fast. Within the next two years, the ratio is expected to reverse … Read more
The meteoric rise of NVIDIA’s GPU technology in accelerating AI has captivated the world and, in the process, given rise to persistent myths. This article tackles 11 of the most common misconceptions about AI processors. Artificial intelligence (AI) has taken the world by storm and integration of AI accelerators and processors into applications is becoming … Read more