Yu-Hsin Chen

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: An uptake of CO2 in the southern extratropical ocean less than that estimated from ocean measurements is found, a result that is not sensitive to transport models or methodological approaches, and carbon fluxes integrated over latitudinal zones are strongly constrained by observations in the middle to high latitudes.

TL;DR: This demonstration could represent the beginning of an era of chip-scale electronic–photonic systems with the potential to transform computing system architectures, enabling more powerful computers, from network infrastructure to data centres and supercomputers.