Building blocks for a temperature-compensated analog VLSI neural network with on-chip learning

Montalvo, A.J.; Gyurcsik, R.S.; Paulos, J.J.

doi:10.1109/iscas.1994.409601

Cited by 7 publications

(1 citation statement)

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“…This one-to-one correspondence allows algorithm development to proceed without hardware in hand, enabling parallel algorithm and hardware progress. In contrast, approaches that use dense analog circuits to model neural components do not maintain one-to-one correspondence between hardware and software, due to device mismatch and statistical fluctuations (e.g., changes in ambient temperature) [4].…”

Section: Introductionmentioning

confidence: 99%

Building block of a programmable neuromorphic substrate: A digital neurosynaptic core

Arthur

Merolla

Akopyan

et al. 2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

110

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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 4.2mm 2 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.

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Section: Introductionmentioning

confidence: 99%