| The Cornell – IBM SyNAPSE team has developed a key building block of a modular neuromorphic architecture: a neurosynaptic core, IBM Almaden scientist Dr. Dharmendra S Modha’s Cognitive Computing Blog reports. |
Modha described IBM's research into Whole Brain Emulation and their plans to simulate the brain by 2018 at the 2008 Singularity Summit.
The core incorporates central elements from nanotechnology, neuroscience and supercomputing, including 256 leaky integrate-and-fire neurons, 1024 axons, and 256x 1024 synapses using an SRAM crossbar memory. It fits in a 4.2mm square area, using a 45nm SOI process.
A design prototype of the core was announced in August 2011, part of the SyNAPSE project, a DARPA program that aims to develop electronic neuromorphic (neuron-like) machine technology similar to the mammalian brain. Such artificial brains would be used in robots whose intelligence matches that of rats, cats, and ultimately even humans.
“One of the main obstacles holding back the widespread utility of low-power neuromorphic chips is the lack of a consistent software-hardware neural programming model, where neuron parameters and connections can be learned off-line to perform a task in software with a guarantee that the same task will run on power-efficient hardware,” the team said in an open-access paper.
The core replaces supercomputers and commodity chips (DSP, GPU, FPGA), both of which require high power consumption, the authors say. The compact design is also compatible with mobile devices. It consumes just 45pJ (picojoule) per spike.
“This is a flexible brain-like architecture capable of a wide array of real-time applications, and designed for the ultra-low power consumption and compact size of biological neural systems,” explained Mohda.
SOURCE KurzweilAI
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