5 papers
CBM-Dual: A 65-nm Fully Connected Chaotic Boltzmann Machine Processor for Dual Function Simulated Annealing and Reservoir Computing
Kanta Yoshioka, Soshi Hirayae, Yuichiro Tanaka +3
This paper presents CBM-Dual, the first silicon-proven digital chaotic dynamics processor (CDP) supporting both simulated annealing (SA) and reservoir computing (RC). CBM-Dual enab…
Hibikino-Musashi@Home 2025 Team Description Paper
Ryohei Kobayashi, Kosei Isomoto, Kosei Yamao +19
This paper provides an overview of the techniques employed by Hibikino-Musashi@Home, which intends to participate in the domestic standard platform league. The team developed a dat…
Harnessing Nonidealities in Analog In-Memory Computing Circuits: A Physical Modeling Approach for Neuromorphic Systems
Yusuke Sakemi, Yuji Okamoto, Takashi Morie +3
Large-scale deep learning models are increasingly constrained by their immense energy consumption, limiting their scalability and applicability for edge intelligence. In-memory com…
Techniques for Enhancing Memory Capacity of Reservoir Computing
Atsuki Yokota, Ichiro Kawashima, Yohei Saito +3
Reservoir Computing (RC) is a bio-inspired machine learning framework, and various models have been proposed. RC is a well-suited model for time series data processing, but there i…
Hibikino-Musashi@Home 2024 Team Description Paper
Kosei Isomoto, Akinobu Mizutani, Fumiya Matsuzaki +25
This paper provides an overview of the techniques employed by Hibikino-Musashi@Home, which intends to participate in the domestic standard platform league. The team has developed a…