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Scaling Mixed-Signal Neuromorphic Processors to 28 nm FD-SOI Technologies


Qiao, Ning; Indiveri, Giacomo (2016). Scaling Mixed-Signal Neuromorphic Processors to 28 nm FD-SOI Technologies. In: Biomedical Circuits and Systems Conference (BioCAS), 2016, Shanghai, 17 October 2016 - 19 October 2016.

Abstract

As processes continue to scale aggressively, the design of deep sub-micron, mixed-signal design is becoming more and more challenging. In this paper we present an analysis of scaling multi-core mixed-signal neuromorphic processors to advanced 28 nm FD-SOI nodes. We address analog design issues which arise from the use of advanced process, including the problem of large leakage currents and device mismatch, and asynchronous digital design issues. We present the outcome of Monte Carlo Analysis and circuit simulations of neuromorphic sub threshold analog/digital neuron circuits which reproduce biologically plausible responses. We describe the AER used to implement PCHB based asynchronous QDI routing processes in multi-core neuromorphic architectures and validate their operation via circuit simulation results. Finally we describe the implementation of custom 28 nm CAM based memory resources utilized in these multi-core neuromorphic processor and discuss the possibility of increasing density by using advanced RRAM devices integrated in the 28 nm Fully-Depleted Silicon on Insulator (FD-SOI) process.

Abstract

As processes continue to scale aggressively, the design of deep sub-micron, mixed-signal design is becoming more and more challenging. In this paper we present an analysis of scaling multi-core mixed-signal neuromorphic processors to advanced 28 nm FD-SOI nodes. We address analog design issues which arise from the use of advanced process, including the problem of large leakage currents and device mismatch, and asynchronous digital design issues. We present the outcome of Monte Carlo Analysis and circuit simulations of neuromorphic sub threshold analog/digital neuron circuits which reproduce biologically plausible responses. We describe the AER used to implement PCHB based asynchronous QDI routing processes in multi-core neuromorphic architectures and validate their operation via circuit simulation results. Finally we describe the implementation of custom 28 nm CAM based memory resources utilized in these multi-core neuromorphic processor and discuss the possibility of increasing density by using advanced RRAM devices integrated in the 28 nm Fully-Depleted Silicon on Insulator (FD-SOI) process.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:19 October 2016
Deposited On:23 Feb 2018 10:01
Last Modified:31 Jul 2018 05:12
Publisher:Biomedical Circuits and Systems Conference (BioCAS), 2016 IEEE
Series Name:Biomedical Circuits and Systems (BIOCAS) 2016
OA Status:Closed
Free access at:Official URL. An embargo period may apply.
Publisher DOI:https://doi.org/10.1109/BioCAS.2016.7833854
Official URL:http://ncs.ethz.ch/pubs/pdf/QiaoIndiveri16.pdf

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