SWANN shuffles weights in 128x128 crossbar arrays to mitigate interconnect resistance, raising ResNet-20/CIFAR-10 accuracy from 47.78% to 83.5% in 7nm 8T-SRAM with under 1% energy overhead.
NEAT: Nonlinearity Aware Training for Accurate, Energy-Efficient, and Robust Implementation of Neural Networks on 1T -1R Crossbars,
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A CTM-GNN model with EnSRF assimilation and flow-weighted transition matrix fuses floating car data and camera observations to deliver physically consistent, network-wide traffic volume estimates and forecasts, demonstrated with improved accuracy in Manhattan.
Simulation study at 7 nm finds FeFET best for large arrays on ResNet-20/CIFAR-10, ReRAM competitive at higher bit-slices on ResNet-50/CIFAR-100, with partial wordline activation and custom ADC levels each raising accuracy by up to ~32%.
citing papers explorer
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WAGONN: Weight Bit Agglomeration in Crossbar Arrays for Reduced Impact of Interconnect Resistance on DNN Inference Accuracy
SWANN shuffles weights in 128x128 crossbar arrays to mitigate interconnect resistance, raising ResNet-20/CIFAR-10 accuracy from 47.78% to 83.5% in 7nm 8T-SRAM with under 1% energy overhead.
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Harnessing Floating Car Data, Traffic Camera Observations, and Network Flow Analysis for Traffic Volume Estimation
A CTM-GNN model with EnSRF assimilation and flow-weighted transition matrix fuses floating car data and camera observations to deliver physically consistent, network-wide traffic volume estimates and forecasts, demonstrated with improved accuracy in Manhattan.
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Comparative Evaluation of Memory Technologies for Synaptic Crossbar Arrays- Part 2: Design Knobs and DNN Accuracy Trends
Simulation study at 7 nm finds FeFET best for large arrays on ResNet-20/CIFAR-10, ReRAM competitive at higher bit-slices on ResNet-50/CIFAR-100, with partial wordline activation and custom ADC levels each raising accuracy by up to ~32%.