Search Results for author:
Found 6 papers, 2 papers with code
Evaluation of STT-MRAM as a Scratchpad for Training in ML Accelerators
We devised a cross-layer simulation framework to evaluate the effectiveness of STT-MRAM as a scratchpad replacing SRAM in a systolic-array-based DNN accelerator.
PIM-DRAM: Accelerating Machine Learning Workloads using Processing in Commodity DRAM
Processing in memory has been proposed as a promising solution for the memory wall bottleneck for ML workloads.
Pruning Filters while Training for Efficiently Optimizing Deep Learning Networks
Our results for VGG-16 trained on CIFAR10 shows that L1 normalization provides the best performance among all the techniques explored in this work with less than 1% drop in accuracy after pruning 80% of the filters compared to the original network.
TxSim:Modeling Training of Deep Neural Networks on Resistive Crossbar Systems
To address this challenge, there is a need for tools that can model the functional impact of non-idealities on DNN training and inference.
Gradual Channel Pruning while Training using Feature Relevance Scores for Convolutional Neural Networks
To address all the above issues, we present a simple-yet-effective gradual channel pruning while training methodology using a novel data-driven metric referred to as feature relevance score.
Proposal for a Leaky-Integrate-Fire Spiking Neuron based on Magneto-Electric Switching of Ferro-magnets
The efficiency of the human brain in performing classification tasks has attracted considerable research interest in brain-inspired neuromorphic computing.
