Author:
Abe Yuki,Nakada Kazuki,Hagiwara Naruki,Suzuki Eiji,Suda Keita,Mochizuki Shin-ichiro,Terasaki Yukio,Sasaki Tomoyuki,Asai Tetsuya
Abstract
AbstractPhysical reservoir computing is a promising solution for accelerating artificial intelligence (AI) computations. Various physical systems that exhibit nonlinear and fading-memory properties have been proposed as physical reservoirs. Highly-integrable physical reservoirs, particularly for edge AI computing, has a strong demand. However, realizing a practical physical reservoir with high performance and integrability remains challenging. Herein, we present an analogue circuit reservoir with a simple cycle architecture suitable for complementary metal-oxide-semiconductor (CMOS) chip integration. In several benchmarks and demonstrations using synthetic and real-world data, our developed hardware prototype and its simulator exhibit a high prediction performance and sufficient memory capacity for practical applications, showing promise for future applications in highly integrated AI accelerators.
Publisher
Springer Science and Business Media LLC
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