随机突触的一个高精度生成向量自回归模型
A High Throughput Generative Vector Autoregression Model for Stochastic Synapses
摘要：
通过模仿大脑的突触连接和可塑性，新兴的电子纳米器件提供了新的机会，作为神经形态系统的构建模块。基于新兴器件的计算架构的大规模仿真的一个挑战是准确地捕获器件响应、滞后、噪声和时域中以及不同器件参数之间的协方差结构。我们解决这个挑战与突触阵列的高吞吐量生成模型，是基于最近可用的类型的电阻存储单元的电气测量数据。我们将这个真实世界的数据映射到一个向量自回归随机过程，以准确地再现设备参数和它们的互相关结构。虽然与测量数据非常匹配，但我们的模型仍然非常快;我们为CPU和GPU提供了并行实现，并展示了超过10亿个单元的阵列大小和每秒超过1亿次权重更新的吞吐量，高于30帧/秒4K视频流的像素速率。
英文摘要：
By imitating the synaptic connectivity and plasticity of the brain, emerging electronic nanodevices offer new opportunities as the building blocks of neuromorphic systems. One challenge for largescale simulations of computational architectures based on emerging devices is to accurately capture device response, hysteresis, noise, and the covariance structure in the temporal domain as well as between the different device parameters. We address this challenge with a high throughput generative model for synaptic arrays that is based on a recently available type of electrical measurement data for resistive memory cells. We map this real world data onto a vector autoregressive stochastic process to accurately reproduce the device parameters and their cross-correlation structure. While closely matching the measured data, our model is still very fast; we provide parallelized implementations for both CPUs and GPUs and demonstrate array sizes above one billion cells and throughputs exceeding one hundred million weight updates per second, above the pixel rate of a 30 frames/s 4K video stream.
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