KIP publications

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.

  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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