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02/01/2023
A^2Q: Aggregation-Aware Quantization for Graph Neural Networks
As graph data size increases, the vast latency and memory consumption du...
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10/27/2022
RePAST: A ReRAM-based PIM Accelerator for Second-order Training of DNN
The second-order training methods can converge much faster than first-or...
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09/30/2022
BayesFT: Bayesian Optimization for Fault Tolerant Neural Network Architecture
To deploy deep learning algorithms on resource-limited scenarios, an eme...
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03/11/2022
DNN Training Acceleration via Exploring GPGPU Friendly Sparsity
The training phases of Deep neural network (DNN) consumes enormous proce...
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03/09/2022
CP-ViT: Cascade Vision Transformer Pruning via Progressive Sparsity Prediction
Vision transformer (ViT) has achieved competitive accuracy on a variety ...
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12/15/2021
N3H-Core: Neuron-designed Neural Network Accelerator via FPGA-based Heterogeneous Computing Cores
Accelerating the neural network inference by FPGA has emerged as a popul...
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03/02/2021
SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network
Resistive Random-Access-Memory (ReRAM) crossbar is a promising technique...
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10/19/2018
Invocation-driven Neural Approximate Computing with a Multiclass-Classifier and Multiple Approximators
Neural approximate computing gains enormous energy-efficiency at the cos...
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07/27/2018
AXNet: ApproXimate computing using an end-to-end trainable neural network
Neural network based approximate computing is a universal architecture p...
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05/23/2018
