MorphBungee: A 65-nm 7.2-mm2 27-μJ/image Digital Edge Neuromorphic Chip with On-Chip 802-frame/s Multi-Layer Spiking Neural Network Learning.

Tengxiao Wang, Min Tian, Haibing Wang, Zhengqing Zhong, Junxian He, Fang Tang, Xichuan Zhou, Yingcheng Lin, Shuang-Ming Yu, Liyuan Liu, Cong Shi
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Abstract

This paper presents a digital edge neuromorphic spiking neural network (SNN) processor chip for a variety of edge intelligent cognitive applications. This processor allows high-speed, high-accuracy and fully on-chip spike-timing-based multi-layer SNN learning. It is characteristic of hierarchical multi-core architecture, event-driven processing paradigm, meta-crossbar for efficient spike communication, and hybrid and reconfigurable parallelism. A prototype chip occupying an active silicon area of 7.2 mm2 was fabricated using a 65-nm 1P9M CMOS process. when running a 256-256-256-256-200 4-layer fully-connected SNN on downscaled 16 × 16 MNIST images. it typically achieved a high-speed throughput of 802 and 2270 frames/s for on-chip learning and inference, respectively, with a relatively low power dissipation of around 61 mW at a 100 MHz clock rate under a 1.0V core power supply, Our on-chip learning results in comparably high visual recognition accuracies of 96.06%, 83.38%, 84.53%, 99.22% and 100% on the MNIST, Fashion-MNIST, ETH-80, Yale-10 and ORL-10 datasets, respectively. In addition, we have successfully applied our neuromorphic chip to demonstrate high-resolution satellite cloud image segmentation and non-visual tasks including olfactory classification and textural news categorization. These results indicate that our neuromorphic chip is suitable for various intelligent edge systems under restricted cost, energy and latency budgets while requiring in-situ self-adaptative learning capability.

MorphBungee:具有片上 802 帧/秒多层尖峰神经网络学习功能的 65 纳米 7.2 mm2 27-μJ/image 数字边缘神经形态芯片。
本文介绍了一种数字边缘神经形态尖峰神经网络(SNN)处理器芯片,适用于各种边缘智能认知应用。该处理器可实现高速、高精度和基于尖峰计时的多层 SNN 学习。它具有分层多核架构、事件驱动处理模式、用于高效尖峰通信的元交叉条以及混合和可重构并行性等特点。当在缩小的 16 × 16 MNIST 图像上运行 256-256-256-256-200 4 层全连接 SNN 时,片上学习和推理的高速吞吐量通常分别达到 802 帧/秒和 2270 帧/秒,而功耗相对较低,在 100 MHz 时钟频率和 1.我们的片上学习在 MNIST、Fashion-MNIST、ETH-80、Yale-10 和 ORL-10 数据集上分别实现了 96.06%、83.38%、84.53%、99.22% 和 100% 的视觉识别准确率。此外,我们还成功应用神经形态芯片演示了高分辨率卫星云图分割和非视觉任务,包括嗅觉分类和纹理新闻分类。这些结果表明,我们的神经形态芯片适用于成本、能耗和延迟预算受限的各种智能边缘系统,同时需要原位自适应学习能力。
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