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：
IEEE Design & Test offers original works describing the models, methods, and tools used to design and test microelectronic systems from devices and circuits to complete systems-on-chip and embedded software. The magazine focuses on current and near-future practice, and includes tutorials, how-to articles, and real-world case studies. The magazine seeks to bring to its readers not only important technology advances but also technology leaders, their perspectives through its columns, interviews, and roundtable discussions. Topics include semiconductor IC design, semiconductor intellectual property blocks, design, verification and test technology, design for manufacturing and yield, embedded software and systems, low-power and energy-efficient design, electronic design automation tools, practical technology, and standards.
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