year | 2016 |
author(s) | Syed Ahmed Aamir, Paul Müller, Andreas Hartel, Johannes Schemmel and Karlheinz Meier |
title | A highly tunable 65-nm CMOS LIF neuron for a large scale neuromorphic system |
KIP-Nummer | HD-KIP 16-82 |
KIP-Gruppe(n) | F9 |
document type | Paper |
Keywords (shown) | Biological system modeling;Biomembranes;Computational modeling;Integrated circuit modeling;Neuromorphics;Neurons;Semiconductor device modeling;65nm CMOS;Analog integrated circuits;Leaky Integrate and Fire;Neuromorphic;Spiking Neuron |
source | Proceedings of IEEE European Solid-State Circuits Conference |
doi | 10.1109/ESSCIRC.2016.7598245 |
Abstract (en) | We present the design and measurement of a continuous-time, accelerated, reconfigurable Leaky Integrate and Fire (LIF) neuron model emulated in 65-nm CMOS technology. The neuron circuit is designed as a sub-circuit of our highly integrated neuromorphic prototype chip, the “HICANN-DLS”. The design is geared towards testability and debug features, as well as area and power efficiency. Each neuron in the array integrates current from a multitude of input synapses onto an RC integrator within the synaptic input sub-circuit, where a variable resistor tunes the synaptic time constant. Linear transconductors convert voltage into an equivalent current as well as modeling the leak term, while a pulse generator circuit evokes a digital spike event. Our measurements show that the neuron successfully integrates input synaptic events ranging from a few nA to greater than 10 µA and tunes a wide range of tunable synaptic and membrane time constants. A higher membrane dynamic range of up to 1100 mV, and longer refractory times can be achieved, operating 1000 times faster than biological real-time. The design of the neuron simplifies calibration and reduces the mismatch, as multiple die measurements indicate. We demonstrate a one-to-one correspondence to software simulation for a typical computational model neuron. Due to the wide tunable range, the neuron is to be our general-purpose element of our second generation flexible neuromorphic platform for a variety of computational models. |
bibtex | @inproceedings{aamir16dlsneuron, author = {S. A. Aamir and P. Müller and A. Hartel and J. Schemmel and K. Meier}, title = {A highly tunable 65-nm CMOS LIF neuron for a large scale neuromorphic system}, booktitle = {IEEE European Solid-State Circuits Conference (ESSCIRC)}, year = {2016}, volume = {}, pages = {71-74}, month = {Sept.} } |
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