Lingxiao Zhu, Wenjie Fan, Chenyang Dai, Shize Zhou, Yongqi Xue, Zhonghai Lu, Li Li, Yuxiang Fu
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A NoC-Based Spatial DNN Inference Accelerator With Memory-Friendly Dataflow
Editor’s notes: This article addresses the challenges of excessive storage overhead and the absence of sparsity-aware design in Network-on-Chip (NoC)-based spatial deep neural network accelerators. The authors present a prototype chip that outperforms existing accelerators in both energy and area efficiency, demonstrated on TSMC 28-nm process technology. —Mahdi Nikdast, Colorado State University, USA —Miquel Moreto, Barcelona Supercomputing Center, Spain —Masoumeh (Azin) Ebrahimi, KTH Royal Institute of Technology, Sweden —Sujay Deb, IIIT Delhi, India
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.