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Paper Abstract and Keywords
Presentation 2026-03-05 14:40
[Memorial Lecture] LMESN: A Leakage-Driven MOSFET Reservoir for Scalable and Ultra-Low-Power Temporal Inference
Haoyuan Li (XJTU/Kyoto Univ.), Masami Utsunomiya, Ryuto Seki, Quan Cheng, Weirong Dong (Kyoto Univ.), Feng Liang (XJTU), Takashi Sato (Kyoto Univ.) VLD2025-94 HWS2025-90 ICD2025-105
Abstract (in Japanese) (See Japanese page) 
(in English) Edge-based temporal inference demands energy-efficient and scalable computing architectures, but existing analog reservoir computing models often face high energy costs and limited reconfigurability. We present LMESN, a leakage-current-driven, pulse-based reservoir computing architecture that exploits intrinsic threshold-voltage variation in standard CMOS to realize ultra-low-power stochastic dynamics. To overcome physical array size constraints, we propose a Shift-Multi-Mask (SMM) technique that emulates large virtual reservoirs through cyclic mask shifts, reducing update energy by over 100× and enabling single-cycle reconfiguration. To further boost task-level performance, we develop a hardware–software co-optimization framework that jointly tunes the ADC quantization range and reservoir mask structure via a discrete genetic algorithm. Post-layout simulations in 22 nm CMOS and evaluations on eight time-series datasets demonstrate up to 13.7% accuracy improvement and 5× variance reduction over unoptimized LMESN baselines. Compared to prior analog and neural network models, LMESN achieves 3–7 orders of magnitude lower energy consumption while delivering competitive or superior accuracy. Together, these innovations make LMESN a scalable, energy-efficient, and task-adaptive platform for edge temporal processing, setting a new direction in physical reservoir computing.
Keyword (in Japanese) (See Japanese page) 
(in English) Reservoir Computing / Echo State Network / Leakage Current / Analog Computing / Optimization Algorithm / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 382, VLD2025-94, pp. 99-105, March 2026.
Paper # VLD2025-94 
Date of Issue 2026-02-25 (VLD, HWS, ICD) 
ISSN Online edition: ISSN 2432-6380
Copyright
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reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF VLD2025-94 HWS2025-90 ICD2025-105

Conference Information
Committee ICD HWS VLD  
Conference Date 2026-03-04 - 2026-03-07 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To VLD 
Conference Code 2026-03-ICD-HWS-VLD 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) LMESN: A Leakage-Driven MOSFET Reservoir for Scalable and Ultra-Low-Power Temporal Inference 
Sub Title (in English)  
Keyword(1) Reservoir Computing  
Keyword(2) Echo State Network  
Keyword(3) Leakage Current  
Keyword(4) Analog Computing  
Keyword(5) Optimization Algorithm  
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Keyword(7)  
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1st Author's Name Haoyuan Li  
1st Author's Affiliation Xi'an Jiaotong University/Kyoto University (XJTU/Kyoto Univ.)
2nd Author's Name Masami Utsunomiya  
2nd Author's Affiliation Kyoto University (Kyoto Univ.)
3rd Author's Name Ryuto Seki  
3rd Author's Affiliation Kyoto University (Kyoto Univ.)
4th Author's Name Quan Cheng  
4th Author's Affiliation Kyoto University (Kyoto Univ.)
5th Author's Name Weirong Dong  
5th Author's Affiliation Kyoto University (Kyoto Univ.)
6th Author's Name Feng Liang  
6th Author's Affiliation Xi'an Jiaotong University (XJTU)
7th Author's Name Takashi Sato  
7th Author's Affiliation Kyoto University (Kyoto Univ.)
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Speaker Author-1 
Date Time 2026-03-05 14:40:00 
Presentation Time 25 minutes 
Registration for VLD 
Paper # VLD2025-94, HWS2025-90, ICD2025-105 
Volume (vol) vol.125 
Number (no) no.382(VLD), no.383(HWS), no.384(ICD) 
Page pp.99-105 
#Pages 7 
Date of Issue 2026-02-25 (VLD, HWS, ICD) 

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