DFR: An Energy-efficient Analog Delay Feedback Reservoir Computing System for Brain-inspired Computing
DFR: An Energy-efficient Analog Delay Feedback Reservoir Computing System for Brain-inspired Computing
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DOI：
10.1145/3264659
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发表时间：
2018-12-01
影响因子：
2.2
通讯作者：
Yi, Yang
中科院分区：
文献类型：
--
作者：
Bai, Kangjun;Yi, Yang
神经形态计算建立在大脑启发的硅芯片上，独特地应用于跟上机器学习算法和数据密度的爆炸性增长。水库计算是一种新兴的计算范式，基于递归神经网络，在多方面的应用中具有已证明的优势，仅在读出阶段提供了一种替代的训练机制。在这项工作中，我们成功地设计和制造了一个节能的模拟延迟反馈水库（DFR）的计算系统，这是建立在一个时间编码方案，非线性传递函数，和一个动态延迟反馈回路。测试结果表明，该系统具有较高的能量效率和丰富的动态特性，使其成为低功耗嵌入式应用的候选者。通过蒙特卡罗仿真，研究和分析了系统的性能，以及鲁棒性。混沌时间序列预测基准，NARMA 10，通过所提出的DFR计算系统进行检查，并表现出减少36%-85%的错误率相比，国家的最先进的DFR计算系统设计。据我们所知，我们的工作代表了DFR计算系统的第一个模拟集成电路（IC）实现。
Neuromorphic computing, which is built on a brain-inspired silicon chip, is uniquely applied to keep pace with the explosive escalation of algorithms and data density on machine learning. Reservoir computing, an emerging computing paradigm based on the recurrent neural network with proven benefits across multifaceted applications, offers an alternative training mechanism only at the readout stage. In this work, we successfully design and fabricate an energy-efficient analog delayed feedback reservoir (DFR) computing system, which is built upon a temporal encoding scheme, a nonlinear transfer function, and a dynamic delayed feedback loop. Measurement results demonstrate its high energy efficiency with rich dynamic behaviors, making the designed system a candidate for low power embedded applications. The system performance, as well as the robustness, are studied and analyzed through the Monte Carlo simulation. The chaotic time series prediction benchmark, NARMA10, is examined through the proposed DFR computing system, and exhibits a 36%-85% reduction on the error rate compared to state-of-the-art DFR computing system designs. To the best of our knowledge, our work represents the first analog integrated circuit (IC) implementation of the DFR computing system.