tag: GPU

Re-Imagining The GPU
By SE Staff - 23 Apr, 2020 - Comments: 0

John Rayfield, CTO at Imagination Technologies, sat down with Semiconductor Engineering to talk about RISC-V, AI, and computing architectures. What follows are excerpts of that conversation. SE: What your plans are for RISC-V? Rayfield: We're actively finalizing the integration of RISC-V cores into future-generation GPUs. That work has been going on for several months. Moving forward, we'... » read more

Tracking Automotive’s Rapidly Shifting Ecosystem
By Ann Mutschler - 02 Apr, 2020 - Comments: 0

The automotive ecosystem is becoming much harder to navigate as automakers, Tier 1s and IP vendors redefine their relationships based upon shifting value caused by an rapidly expanding amount of increasingly interdependent and complex electronic content. Predictions of massive change started almost a decade ago with a number of pilot programs around autonomous vehicles. But those shifts real... » read more

Software-Defined Hardware Gains Ground — Again
By Ann Mutschler - 26 Mar, 2020 - Comments: 0

The traditional approach of running generic software on x86-based CPUs is running out of steam for many applications due to the slowdown of Moore’s Law and the concurrent exponential growth in software application complexity and scale. In this environment, the software and hardware are disparate due the dominance of the x86 architecture. “The need for and advent of the hardware accelerat... » read more

What Machine Learning Can Do In Fabs
By Mark LaPedus - 25 Mar, 2020 - Comments: 0

Semiconductor Engineering sat down to discuss the issues and challenges with machine learning in semiconductor manufacturing with Kurt Ronse, director of the advanced lithography program at Imec; Yudong Hao, senior director of marketing at Onto Innovation; Romain Roux, data scientist at Mycronic; and Aki Fujimura, chief executive of D2S. What follows are excerpts of that conversation. L-R:... » read more

Machine Learning At The Edge
By Mentor, a Siemens Business - 11 Mar, 2020 - Comments: 0

Moving machine learning to the edge has critical requirements on power and performance. Using off-the-shelf solutions is not practical. CPUs are too slow, GPUs/TPUs are expensive and consume too much power, and even generic machine learning accelerators can be overbuilt and are not optimal for power. In this paper, learn about creating new power/memory efficient hardware architectures to meet n... » read more

High-Performance Memory For AI And HPC
By Ed Sperling - 09 Mar, 2020 - Comments: 0

Frank Ferro, senior director of product management at Rambus, examines the current performance bottlenecks in high-performance computing, drilling down into power and performance for different memory options, and explains what are the best solutions for different applications and why. » read more

New Architectural Issues Facing Auto Ecosystem
By Ann Mutschler - 05 Mar, 2020 - Comments: 0

As chips bound for the automotive world move to small process nodes, including 5nm and below, the automotive ecosystem is wrestling with both scaling issues and challenges related to architecting safety-critical systems using fewer chips. This may sound counterintuitive, because one of the main reasons automotive chip providers are moving to smaller nodes is to reduce the number of chips in ... » read more

The Challenges Of Building Inferencing Chips
By Ann Mutschler - 27 Feb, 2020 - Comments: 0

Putting a trained algorithm to work in the field is creating a frenzy of activity across the chip world, spurring designs that range from purpose-built specialty processors and accelerators to more generalized extensions of existing and silicon-proven technologies. What's clear so far is that no single chip architecture has been deemed the go-to solution for inferencing. Machine learning is ... » read more

PowerPR Virtualization: A Critical Feature For Automotive GPUs
By Imagination Technologies - 26 Feb, 2020 - Comments: 0

What is GPU virtualization? Conceptually, virtualization is the capability of a device to host one or more virtual machines (VMs) that each behave like actual independent machines with their own operating system (OS), all running on the same underlying device hardware. In regard to GPUs, this means the capability to support multiple concurrently running operating systems, each capable of submit... » read more

Chiplet Momentum Rising
By Mark LaPedus - 26 Feb, 2020 - Comments: 0

The chiplet model is gaining momentum as an alternative to developing monolithic ASIC designs, which are becoming more complex and expensive at each node. Several companies and industry groups are rallying around the chiplet model, including AMD, Intel and TSMC. In addition, there is a new U.S. Department of Defense (DoD) initiative. The goal is to speed up time to market and reduce the cost... » read more

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