WATTLE: A Trainable Gain Analogue VLSI Neural Network

Part of Advances in Neural Information Processing Systems 6 (NIPS 1993)

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Richard Coggins, Marwan Jabri


This paper describes a low power analogue VLSI neural network called Wattle. Wattle is a 10:6:4 three layer perceptron with multi(cid:173) plying DAC synapses and on chip switched capacitor neurons fabri(cid:173) cated in 1.2um CMOS. The on chip neurons facillitate variable gain per neuron and lower energy/connection than for previous designs. The intended application of this chip is Intra Cardiac Electrogram classification as part of an implantable pacemaker / defibrillator sys(cid:173) tem. Measurements of t.he chip indicate that 10pJ per connection is achievable as part of an integrated system. Wattle has been suc(cid:173) cessfully trained in loop on parity 4 and ICEG morphology classi(cid:173) fication problems.