PowerPR Virtualization: A Critical Feature For Automotive GPUs


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


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

Understanding SLAM (Simultaneous Localization And Mapping)


Amol Borkar, senior product manager for AI and computer vision at Cadence, talks with Semiconductor Engineering about mapping and tracking the movement of an object in a scene, how to identify key corners in a frame, how probabilities of accuracy fit into the picture, how noise can affect that, and how to improve the performance and reduce power in these systems. » read more

Uses, Limits And Questions For FPGAs In Autos


Programmable logic in automotive applications is essential, given the parade of almost constant updates and shifts in direction, but exactly where the technology will be used has become a moving target. This isn't entirely surprising in the automotive industry. Carmakers are moving into electrification and increasing levels of automation in fits and starts, sometimes with dramatic swings in ... » read more

Divided On System Partitioning


Building an optimal implementation of a system using a functional description has been an industry goal for a long time, but it has proven to be much more difficult than it sounds. The general idea is to take software designed to run on a processor and to improve performance using various types of alternative hardware. That performance can be specified in various ways and for specific applic... » read more

A New Dawn For IP


The IP industry is changing again. The concept started as build once, use everywhere, but today it is more like architect once, customize everywhere. Few designs can afford sub-optimal IP for their application. The need for customized IP is driven by both leading-edge designs and the trailing markets, although for different reasons. While this customization is causing IP companies to transfo... » read more

Checkmate: Breaking The Memory Wall With Optimal Tensor Rematerialization


Source: Published on arXiv 10/7/ 2019   Paras Jain Ajay Jain Aniruddha Nrusimha Amir Gholami Pieter Abbeel Kurt Keutzer Ion Stoica Joseph E. Gonzalez A recent paper published on arXiv by a team of UC Berkeley researchers notes that neural networks are increasingly impeded by the limited capacity of on-device GPU memory. The UC Berkeley team uses off-the-shel... » read more

Multi-Patterning EUV Vs. High-NA EUV


Foundries are finally in production with EUV lithography at 7nm, but chip customers must now decide whether to implement their next designs using EUV-based multiple patterning at 5nm/3nm or wait for a new single-patterning EUV system at 3nm and beyond. This scenario revolves around ASML’s current extreme ultraviolet (EUV) lithography tool (NXE:3400C) versus a completely new EUV system with... » read more

Using FPGAs For AI


Artificial intelligence (AI) and machine learning (ML) are progressing at a rate that is outstripping Moore's Law. In fact, they now are evolving faster than silicon can be designed. The industry is looking at all possibilities to provide devices that have the necessary accuracy and performance, as well as a power budget that can be sustained. FPGAs are promising, but they also have some sig... » read more

Making Sense Of Inferencing Options


Ian Bratt, fellow in Arm’s machine learning group, sheds light on all the different processing elements in machine learning, how different end user requirements affect those choices, why CPUs are a critical element in orchestrating what happens in these systems, and how power and software play into these choices. » read more

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