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June 19, 2012

Building Block of a Programmable Neuromorphic Substrate: A Digital Neurosynaptic Core

Last week, IBM-Cornell SyNAPSE Team published the following paper:

Citation: John V. Arthur, Paul A. Merolla, Filipp Akopyan, Rodrigo Alvarez-Icaza, Andrew Cassidy, Shyamal Chandra, Steven K. Esser, Nabil Imam, William Risk, Daniel Rubin, Rajit Manohar, and Dharmendra S. Modha, "Building Block of a Programmable Neuromorphic Substrate: A Digital Neurosynaptic Core", International Joint Conference on Neural Networks, June 2012.

Abstract: The grand challenge of neuromorphic computation is to develop a flexible brain-like architecture capable of a wide array of real-time applications, while striving towards the ultra-low power consumption and compact size of biological neural systems. To this end, we fabricated a key building block of a modular neuromorphic architecture, a neurosynaptic core. Our implementation consists of 256 integrate-and-fire neurons and a 1,024x256 SRAM crossbar memory for synapses that fits in 4.2mm2 using a 45nm SOI process and consumes just 45pJ per spike. The core is fully configurable in terms of neuron parameters, axon types, and synapse states and its fully digital implementation achieves one-to-one correspondence with software simulation models. One-to-one correspondence allows us to introduce an abstract neural programming model for our chip, a contract guaranteeing that any application developed in software functions identically in hardware. This contract allows us to rapidly test and map applications from control, machine vision, and classification. To demonstrate, we present four test cases (i) a robot driving in a virtual environment, (ii) the classic game of pong, (iii) visual digit recognition and (iv) an autoassociative memory.

June 06, 2012

Implementation of olfactory bulb glomerular-layer computations in a digital neurosynaptic core

Today, Cornell - IBM SyNAPSE Team published the following paper:

Citation: Imam N, Cleland TA, Manohar R, Merolla PA, Arthur JV, Akopyan F and Modha DS (2012) Implementation of olfactory bulb glomerular-layer computations in a digital neurosynaptic core. Front. Neurosci. 6:83. doi: 10.3389/fnins.2012.00083

Abstract: We present a biomimetic system that captures essential functional properties of the glomerular layer of the mammalian olfactory bulb, specifically including its capacity to decorrelate similar odor representations without foreknowledge of the statistical distributions of analyte features. Our system is based on a digital neuromorphic chip consisting of 256 leaky-integrate-and-fire neurons, 1024 × 256 crossbar synapses, and address-event representation communication circuits. The neural circuits configured in the chip reflect established connections among mitral cells, periglomerular cells, external tufted cells, and superficial short-axon cells within the olfactory bulb, and accept input from convergent sets of sensors configured as olfactory sensory neurons. This configuration generates functional transformations comparable to those observed in the glomerular layer of the mammalian olfactory bulb. Our circuits, consuming only 45 pJ of active power per spike with a power supply of 0.85 V, can be used as the first stage of processing in low-power artificial chemical sensing devices inspired by natural olfactory systems.

June 02, 2012

The Cognitive Systems Era

Youtube Video (5 minutes and 16 seconds) describing my team's work in the context of IBM's Cognitive Systems Era: http://www.youtube.com/watch?v=gQ3HEVelBFY