Optimization Driving Changes In Microarchitectures

By Ann Mutschler - 23 Sep, 2021 - Comments: 0

The semiconductor ecosystem is at a turning point for how to best architect the CPU based on the explosion of data, the increased usage of AI, and the need for differentiation and customization in leading-edge applications. In the past, much of this would have been accomplished by moving to the next process node. But with the benefits from scaling diminishing at each new node, the focus is s... » read more

SpZip: Architectural Support for Effective Data Compression In Irregular Applications

By Technical Paper Link - 11 Aug, 2021 - Comments: 0

Technical paper link is here. Published in: 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA) Yifan Yang (MIT); Joel Emer (MIT / NVIDIA); Daniel Sanchez (MIT) Abstract: "Irregular applications, such as graph analytics and sparse linear algebra, exhibit frequent indirect, data-dependent accesses to single or short sequences of elements that cause high ma... » read more

Efficient Multi-GPU Shared Memory via Automatic Optimization of Fine-Grained Transfers

By Technical Paper Link - 02 Jul, 2021 - Comments: 0

Harini Muthukrishnan (U of Michigan); David Nellans, Daniel Lustig (NVIDIA); Jeffrey A. Fessler, Thomas Wenisch (U of Michigan). Abstract—"Despite continuing research into inter-GPU communication mechanisms, extracting performance from multiGPU systems remains a significant challenge. Inter-GPU communication via bulk DMA-based transfers exposes data transfer latency on the GPU’s critical... » read more

Thermal Challenges And Moore’s Law

By Ed Sperling - 22 Oct, 2019 - Comments: 0

Steven Woo, fellow and distinguished inventor at Rambus, looks at the evolution of graphics cards over a couple of decades and how designs changed to deal with more graphics and more heat, and why smaller, faster and cheaper doesn’t apply in this market. » read more

Machine Learning Inferencing At The Edge

By Ed Sperling - 12 Sep, 2019 - Comments: 1

Ian Bratt, fellow in Arm's machine learning group, talks about why machine learning inferencing at the edge is so difficult, what are the tradeoffs, how to optimize data movement, how to accelerate that movement, and how it differs from developing other types of processors. » read more

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tag: data movement

Optimization Driving Changes In Microarchitectures

SpZip: Architectural Support for Effective Data Compression In Irregular Applications

Efficient Multi-GPU Shared Memory via Automatic Optimization of Fine-Grained Transfers

Thermal Challenges And Moore’s Law

Machine Learning Inferencing At The Edge