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 1,024×256 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.