by Simla Burcu Harma, Mario Drumond, Babak Falsafi on Sep 20, 2021 | Tags: Accelerators, Machine Learning, Numerical Format, Tools
DNN training is emerging as a popular compute-intensive workload. This blog post provides an overview of the recent research on numerical encoding formats for DNN training, and presents the Hybrid Block Floating-Point (HBFP) format which reduces silicon provisioning...
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by Tim Rogers and Mahmoud Khairy on Aug 10, 2021 | Tags: Accelerators, Benchmarks, Machine Learning, Systems
At its core, all engineering is science optimized (or perverted) by economics. As academics in computer science and engineering, we have a symbiotic relationship with industry. Still, it is often necessary for us to peel back the marketing noise and understand...
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by Yuhao Zhu on Jul 6, 2021 | Tags: Accelerators, deep learning, gpu, ray tracing, rendering
In Part I of this mini-series, we looked at recent advances in hardware support for ray tracing and how we might ride this wave to think more broadly about general-purpose irregular computing. Part II looks at another rising trend in graphics, i.e., the confluence of...
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by Yuhao Zhu on Jul 1, 2021 | Tags: Accelerators, gpu, graphics, ray tracing, rendering
Introduction Computer graphics exemplifies hardware-software co-design. Since its inception, rendering algorithms have been developed hand in hand with hardware architecture. Graphics is only becoming more important with the rise of new visual applications such as...
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by Adrian Sampson on Jun 29, 2021 | Tags: Accelerators, Programmability, Programming Languages
We need to make it easier to design custom, application-specific hardware accelerators. The potential efficiency gains are enormous, and the cost of deploying accelerators is falling rapidly with the widespread availability of FPGA cards and the increasing...
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