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Found 15 papers, 1 papers with code
Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons
Deep Neural Networks (DNNs) have revolutionized a wide range of industries, from healthcare and finance to automotive, by offering unparalleled capabilities in data analysis and decision-making.
Approximate Computing and the Efficient Machine Learning Expedition
In this work, we enlighten the synergistic nature of AxC and ML and elucidate the impact of AxC in designing efficient ML systems.
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization
We report on aggressive quantization strategies that greatly accelerate inference of Recurrent Neural Network Transducers (RNN-T).
InterTrain: Accelerating DNN Training using Input Interpolation
This is because the efficacy of learning on interpolated inputs is reduced by the interference between the forward/backward propagation of their constituent inputs.
4-bit Quantization of LSTM-based Speech Recognition Models
We investigate the impact of aggressive low-precision representations of weights and activations in two families of large LSTM-based architectures for Automatic Speech Recognition (ASR): hybrid Deep Bidirectional LSTM - Hidden Markov Models (DBLSTM-HMMs) and Recurrent Neural Network - Transducers (RNN-Ts).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
Large-scale distributed training of Deep Neural Networks (DNNs) on state-of-the-art platforms is expected to be severely communication constrained.
Accelerating DNN Training through Selective Localized Learning
The trend in the weight updates made to the transition layer across epochs is used to determine how the boundary betweenSGD and localized updates is shifted in future epochs.
Ultra-Low Precision 4-bit Training of Deep Neural Networks
In this paper, we propose a number of novel techniques and numerical representation formats that enable, for the very first time, the precision of training systems to be aggressively scaled from 8-bits to 4-bits.
Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks
Reducing the numerical precision of data and computation is extremely effective in accelerating deep learning training workloads.
Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN)
Deep learning algorithms achieve high classification accuracy at the expense of significant computation cost.
PACT: Parameterized Clipping Activation for Quantized Neural Networks
We show, for the first time, that both weights and activations can be quantized to 4-bits of precision while still achieving accuracy comparable to full precision networks across a range of popular models and datasets.
SparCE: Sparsity aware General Purpose Core Extensions to Accelerate Deep Neural Networks
SparCE consists of 2 key micro-architectural enhancements- a Sparsity Register File (SpRF) that tracks zero registers and a Sparsity aware Skip Address (SASA) table that indicates instructions to be skipped.
DyVEDeep: Dynamic Variable Effort Deep Neural Networks
Complementary to these approaches, DyVEDeep is a dynamic approach that exploits the heterogeneity in the inputs to DNNs to improve their compute efficiency with comparable classification accuracy.
Multiplier-less Artificial Neurons Exploiting Error Resiliency for Energy-Efficient Neural Computing
Multipliers consume most of the processing energy in the digital neurons, and thereby in the hardware implementations of artificial neural networks.
Energy-Efficient Object Detection using Semantic Decomposition
We propose a 2-stage hierarchical classification framework, with increasing levels of complexity, wherein the first stage is trained to recognize the broad representative semantic features relevant to the object of interest.
