Fig. 4: Performance of DenRAM on Heartbeat Anomaly Detection and Keyword Spotting.
From: DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays
a Classification accuracy as a function of the number of synapses per dendritic branch and comparison with a SRNN of 32 neurons (1.1k parameters). Error bars capture the standard deviation over 10 trials. b Memory Footprint of the DenRAM architecture solving ECG, compared with an iso-accuracy SRNN. c Power consumption of DenRAM in the ECG task and comparison an iso-accuracy SRNN. d Classification accuracy as a function of the noise introduced on the weights for two delay architectures (D1: 700 inputs, 16 delays; D2: 256 inputs, 16 delays) and for two SRNN architectures with one hidden layer (R1: 700 inputs, 235 hidden neurons; R2: 256 inputs, 180 hidden neurons). e Classification accuracy (with RRAM-calibrated noise on the weights) concerning the network’s number of parameters for D1, D2 and R2, sweeping the number of synapses per branch for D1 and D2, the number of hidden neurons for R2. f Power consumption of each network configuration (D1, D2 and R2) shown in e). In d–f error bars represent the standard deviation over 3 trials.
