Neuromorphic Recurrent Spiking Neural Networks for EMG Gesture Classification and Low Power Implementation on Loihi
Sai Sukruth Bezugam, A. Shaban, Manan Suri
Abstract
In this work, we show an efficient Electromyograph (EMG) gesture recognition using Double Exponential Adaptive Threshold (DEXAT) neuron based Recurrent Spiking Neural Network (RSNN). Our network achieves a classification accuracy of 90% while using lesser number of neurons compared to the best reported prior art on Roshambo EMG dataset. Further, to illustrate the benefits of dedicated neuromorphic hardware, we show hardware implementation of DEXAT neuron using multicompartment methodology on Intel's neuromorphic Loihi chip. RSNN implementation on Loihi (Nahuku 32) achieves significant energy/latency benefits of ~983X/19X compared to GPU for batch size = 50.
Topics & Concepts
Neuromorphic engineeringSpiking neural networkComputer scienceArtificial neural networkPower (physics)NeuroscienceArtificial intelligenceComputer architecturePsychologyQuantum mechanicsPhysicsAdvanced Memory and Neural ComputingEEG and Brain-Computer InterfacesNeural Networks and Reservoir Computing