A 65-nm Neuromorphic Image Classification Processor With Energy-Efficient Training Through Direct Spike-Only Feedback
A 65-nm Neuromorphic Image Classification Processor With Energy-Efficient Training Through Direct Spike-Only Feedback
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DOI：
10.1109/jssc.2019.2942367
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发表时间：
2020-01-01
影响因子：
5.4
通讯作者：
Jeon, Dongsuk
中科院分区：
文献类型：
--
作者：
Park, Jeongwoo;Lee, Juyun;Jeon, Dongsuk
神经网络（NN）和机器学习算法的最新进展引发了专用硬件的广泛研究，从高性能NN加速器内在服务器系统内使用到节能边缘计算系统。尽管这些研究大多数都集中在设计推理引擎上，但由于需要更高的数值精度，因此实施NN的训练过程仍然是一个挑战。在本文中，我们旨在建立一个片上学习系统，该系统将对NNS显示高能的培训，而不会在机器学习任务的性能中退化。为了实现这一目标，我们适应和优化了神经形态学习算法，并提出了硬件设计技术以充分利用修饰的属性。我们验证我们的系统可以实现节能训练，而在手写数字[改良的国家标准和技术数据库（MNIST）图像上，其高效推断了236 NJ/图像的高效推断，其能源消耗仅多7.5。此外，我们的系统在MNIST测试数据集上达到了97.83的分类精度，该数据集优于先前的神经形态片上学习系统，并且接近训练深神经网络（NNS）的传统方法的性能。
Recent advances in neural network (NN) and machine learning algorithms have sparked a wide array of research in specialized hardware, ranging from high-performance NN accelerators for use inside the server systems to energy-efficient edge computing systems. While most of these studies have focused on designing inference engines, implementing the training process of an NN for energy-constrained mobile devices has remained to be a challenge due to the requirement of higher numerical precision. In this article, we aim to build an on-chip learning system that would show highly energy-efficient training for NNs without degradation in the performance for machine learning tasks. To achieve this goal, we adapt and optimize a neuromorphic learning algorithm and propose hardware design techniques to fully exploit the properties of the modifications. We verify that our system achieves energy-efficient training with only 7.5 more energy consumption compared with its highly efficient inference of 236 nJ/image on the handwritten digit [Modified National Institute of Standards and Technology database (MNIST)] images. Moreover, our system achieves 97.83 classification accuracy on the MNIST test data set, which outperforms prior neuromorphic on-chip learning systems and is close to the performance of the conventional method for training deep neural networks (NNs), the backpropagation.