AI Inference Memory System Tradeoffs


When companies describe their AI inference chip they typically give TOPS but don’t talk about their memory system, which is equally important. What is TOPS? It means Trillions or Tera Operations per Second. It is primarily a measure of the maximum achievable throughput but not a measure of actual throughput. Most operations are MACs (multiply/accumulates), so TOPS = (number of MAC units) x... » read more

Semicon West Debrief


AI vs. energy. Quantum for everyone. Biofabrication of human organs on a mass scale. Slowing advancements from Moore’s law. In the midst of a market dip, optimism reigned as keynote and AI Design Forum speakers addressed both looming challenges and explosive market opportunities during July 9-10 presentations at SEMICON West 2019 in San Francisco. SEMICON West again proved to be a magnet f... » read more

Week in Review: IoT, Security, Autos


Products/Services Siemens announced that Mazda Motor adopted the Capital electrical design software suite from Mentor, a Siemens Business, for the design of next-generation automotive electrical systems. Mazda is said to use Capital for model-based generative design for the electrical and electronic systems of the entire vehicle platform. Synopsys will host the 11th annual Codenomi-con USA ... » read more

Hardware-Software Co-Design Reappears


The core concepts in hardware-software co-design are getting another look, nearly two decades after this approach was first introduced and failed to catch on. What's different this time around is the growing complexity and an emphasis on architectural improvements, as well as device scaling, particularly for AI/ML applications. Software is a critical component, and the more tightly integrate... » read more

Using Memory Differently To Boost Speed


Boosting memory performance to handle a rising flood of data is driving chipmakers to explore new memory types and different ways of using existing memory, but it also is creating some complex new challenges. For most of the semiconductor design industry, memory has been a non-issue for the past couple of decades. The main concerns were price and size, but memory makers have been more than a... » read more

In Memory And Near-Memory Compute


Steven Woo, Rambus fellow and distinguished inventor, talks about the amount of power required to store data and to move it out of memory to where processing is done. This can include changes to memory, but it also can include rethinking compute architectures from the ground up to achieve up to 1 million times better performance in highly specialized systems. Related Find more memor... » read more

Week in Review: IoT, Security, Auto


Products/Services Arm rolled out its Flexible Access program, which offers system-on-a-chip design teams the capability to try out the company’s semiconductor intellectual property, along with IP from Arm partners, before they commit to licensing IP and to pay only for what they use in production. The new engagement model is expected to prove useful for Internet of Things design projects and... » read more

GDDR Accelerates Artificial Intelligence And Machine Learning


The origins of modern graphics double data rate (GDDR) memory can be traced back to GDDR3 SDRAM. Designed by ATI Technologies, GDDR3 made its first appearance in NVidia’s GeForce FX 5700 Ultra card which debuted in 2004. Offering reduced latency and high bandwidth for GPUs, GDDR3 was followed by GDDR4, GDDR5, GDDR5X and the latest generation of GDDR memory, GDDR6. GDDR6 SGRAM supports a ma... » read more

Low-Power Design Becomes Even More Complex


Throughout the SoC design flow, there has been a tremendous amount of research done to ease the pain of managing a long list of power-related issues. And while headway has been made, the addition of new application areas such as AI/ML/DL, automotive and IoT has raised as many new problems as have been solved. The challenges are particularly acute at leading-edge nodes where devices are power... » read more

What’s Powering Artificial Intelligence


To scale artificial intelligence (AI) and machine learning (ML), hardware and software developers must enable AI/ML performance across a vast array of devices. This requires balancing the need for functionality alongside security, affordability, complexity and general compute needs. Fortunately, there’s a solution hiding in plain sight. To read more, click here (scroll down to "Download No... » read more

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