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非同期型ブロック構造ニューロンのFPGA向けハードウェア実装手法

李 建道 横浜国立大学 DOI:info:doi/10.18880/00013923

2021.06.17

概要

With the growing expectations for Cyber-Physical Systems (CPS) and the Internet of things (IoT) in recent years, there is a need for the edge and fog computing technologies. Especially for the realization of a smart society with Artificial Intelligence (AI), smart devices that perform intelligent processing with end-to-end computational resources are essential. Field Programmable Gate Arrays (FPGAs) have been attracting attention as an effective means to realize such devices. Although the flexibility of FPGAs, there are still many restrictions to realize Machine-Learning algorithm on the hardware. In response to this problem, Block-Based Neural Network (BBNN) architecture proposed by Moon et al. is a simple way to reconfigure NN by representing the circuit configuration of the network as a block, which is an effective algorithm to implement such intelligent devices in FPGA. However, the current Genetic Algorithm (GA) used for its optimal configuration, including the network structure, has a problem that it is difficult to achieve practical applications in terms of both design cost and speed. In this study, these problems are solved by two new techniques. First, we propose an Asynchronous BlockBased Neuron (ABBN), in which a new basic block with a high degree of freedom is designed to improve the speed of operation, and then the pipeline architecture is used to accelerate it. This method enables on-line optimization of configuration and significantly improves the performance compared to the conventional BBNN. Next, to solve the problem of optimization efficiency of GA itself, we devised Block-Based Reservoir Computing (BBRC), which uses ABBNs to construct reservoirs, and BBRC does not include the optimization process of GA, but only performs optimization with a single linear regression estimation, so it is extremely fast. With these methods, technological advances have been made, contribute to the practical application of BBNN.

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