Joel Emer

TL;DR: In this paper, the authors provide a comprehensive tutorial and survey about the recent advances toward the goal of enabling efficient processing of DNNs, and discuss various hardware platforms and architectures that support DNN, and highlight key trends in reducing the computation cost of deep neural networks either solely via hardware design changes or via joint hardware and DNN algorithm changes.

TL;DR: Eyeriss as mentioned in this paper is an accelerator for state-of-the-art deep convolutional neural networks (CNNs) that optimizes for the energy efficiency of the entire system, including the accelerator chip and off-chip DRAM, by reconfiguring the architecture.

TL;DR: A novel dataflow, called row-stationary (RS), is presented, that minimizes data movement energy consumption on a spatial architecture and can adapt to different CNN shape configurations and reduces all types of data movement through maximally utilizing the processing engine local storage, direct inter-PE communication and spatial parallelism.

TL;DR: This paper identifies numerous cases, such as prefetches, dynamicallydead code, and wrong-path instructions, in which a fault will not affect correct execution, and shows AVFs of 28% and 9% for the instruction queue and execution units, respectively,averaged across dynamic sections of the entire CPU2000benchmark suite.

TL;DR: This paper presents an architecture for simultaneous multithreading that minimizes the architectural impact on the conventional superscalar design, has minimal performance impact on a single thread executing alone, and achieves significant throughput gains when running multiple threads.