Getting Optimal PPA For HPC & AI Applications With Foundation IP


By Andrew Appleby, Xiaorui Hu, and Bhavana Chaurasia The demand for application-specific system-on-chips (SoCs) for compute applications is ever-increasing. Today, the diversity of requirements means there is a need for a rich set of compute solutions in a wide range of process technologies. The resulting products may have very different but demanding power, performance, and area (PPA) requi... » read more

AI-Driven Macro Placement Boosts PPA


In the era of EDA 4.0, artificial intelligence (AI) and machine learning (ML) are transforming what electronic design automation tools are capable of. For many of the challenges of physical IC design, AI can provide significant benefits to both the turnaround time and the quality of the design, as measured by performance, power, and area (PPA) metrics. One implementation step due for improve... » read more

BYO NPU Benchmarks


In our last blog post, we highlighted the ways that NPU vendors can shade the truth about performance on benchmark networks such that comparing common performance scores such as “Resnet50 Inferences / Second” can be a futile exercise. But there is a straight-forward, low-investment method for an IP evaluator to short-circuit all the vendor shenanigans and get a solid apples-to-apples result... » read more

The Journey To Exascale Computing And Beyond


High performance computing witnessed one of its most ambitious leaps forward with the development of the US supercomputer “Frontier.” As Scott Atchley from Oak Ridge National Laboratory discussed at Supercomputing 23 (SC23) in Denver last month, the Frontier had the ambitious goal of achieving performance levels 1000 times higher than the petascale systems that preceded it, while also stayi... » read more

Requirements For The Efficient Implementation Of AI Solutions On Edge Devices


By André Schneider, Olaf Enge-Rosenblatt, and Björn Zeugmann In recent years, there has been a growing tendency to implement data-driven approaches for the continuous monitoring of industrial plants as part of digitalization and Industry 4.0 initiatives. The hope is to detect critical conditions at an early stage, minimize maintenance and downtimes, and continuously achieve high product qu... » read more

Advanced Design Debug Demands Integrated Verification Management


Design verification has been the dominant portion of chip development for years, and the challenges grow bigger every day. Single dies continue to grow in transistor count and complexity. Advanced techniques such as 2.5D and 3D multi-die systems and emerging technologies such as wafer-scale integration pack even more transistors and functionality into a single device. This situation has created... » read more

What Is SMU Digitizer Mode And Why Is It Useful?


Some source/measure unit (SMU) products support a feature called “digitizer mode.” However, from the title alone it is not obvious exactly what this feature means. This capability is related to the triggering system of the SMU, so it is helpful to review the ARM-TRIGGER model first. Keysight products supporting the standard commands for programmable instrument (SCPI) adhere to the trigge... » read more

Decoding GDS To Thermal Model Conversion


Driven by Moore’s Law and modern, ubiquitous computation power demand, the market will continue to demand higher chip performance. Therefore, modern chips with ever-higher power densities present critical thermal challenges. With the ever-shrinking design margins, designers must manage their thermal budget at every stage of the design, from chip to system. Now, let us shift left and start ... » read more

Imagining A Hydrogen-Powered Future Thanks To Simulation


Our world leaders have set ambitious goals for global decarbonization by 2050, with good reason. While the planet continues to heat up, global energy consumption is still rising, and most of this energy consumption is currently derived from fossil fuels. This bitter truth has inspired a wave of decarbonization trends in response — including green hydrogen, which is generated using renewable ... » read more

Considerations For Accelerating On-Device Stable Diffusion Models


One of the more powerful – and visually stunning – advances in generative AI has been the development of Stable Diffusion models. These models are used for image generation, image denoising, inpainting (reconstructing missing regions in an image), outpainting (generating new pixels that seamlessly extend an image's existing bounds), and bit diffusion. Stable Diffusion uses a type of dif... » read more

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