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Found 11 papers, 2 papers with code
A2Q+: Improving Accumulator-Aware Weight Quantization
Recent studies show that also reducing the precision of the accumulator can further improve hardware efficiency at the risk of numerical overflow, which introduces arithmetic errors that can degrade model accuracy.
A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidance
We apply our method to deep learning-based computer vision tasks to show that A2Q can train QNNs for low-precision accumulators while maintaining model accuracy competitive with a floating-point baseline.
Quantized Neural Networks for Low-Precision Accumulation with Guaranteed Overflow Avoidance
Across all of our benchmark models trained with 8-bit weights and activations, we observe that constraining the hidden layers of quantized neural networks to fit into 16-bit accumulators yields an average 98. 2% sparsity with an estimated compression rate of 46. 5x all while maintaining 99. 2% of the floating-point performance.
Robust Transferable Feature Extractors: Learning to Defend Pre-Trained Networks Against White Box Adversaries
The widespread adoption of deep neural networks in computer vision applications has brought forth a significant interest in adversarial robustness.
Human-Like Navigation Behavior: A Statistical Evaluation Framework
Recent advancements in deep reinforcement learning have brought forth an impressive display of highly skilled artificial agents capable of complex intelligent behavior.
Generating GPU Compiler Heuristics using Reinforcement Learning
GPU compilers are complex software programs with many optimizations specific to target hardware.
Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations
State-of-the-art quantization techniques are currently applied to both the weights and activations; however, pruning is most often applied to only the weights of the network.
An Energy-Efficient Edge Computing Paradigm for Convolution-based Image Upsampling
We analyze and compare the inference properties of convolution-based upsampling algorithms using a quantitative model of incurred time and energy costs and show that using deconvolution for inference at the edge improves both system latency and energy efficiency when compared to their sub-pixel or resize convolution counterparts.
Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework
The use of Deep Learning hardware algorithms for embedded applications is characterized by challenges such as constraints on device power consumption, availability of labeled data, and limited internet bandwidth for frequent training on cloud servers.
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems
Stochastic-sampling-based Generative Neural Networks, such as Restricted Boltzmann Machines and Generative Adversarial Networks, are now used for applications such as denoising, image occlusion removal, pattern completion, and motion synthesis.
AX-DBN: An Approximate Computing Framework for the Design of Low-Power Discriminative Deep Belief Networks
The power budget for embedded hardware implementations of Deep Learning algorithms can be extremely tight.
