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08/25/2023
A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidance
We present accumulator-aware quantization (A2Q), a novel weight quantiza...
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01/31/2023
Quantized Neural Networks for Low-Precision Accumulation with Guaranteed Overflow Avoidance
We introduce a quantization-aware training algorithm that guarantees avo...
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09/14/2022
Robust Transferable Feature Extractors: Learning to Defend Pre-Trained Networks Against White Box Adversaries
The widespread adoption of deep neural networks in computer vision appli...
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03/10/2022
Human-Like Navigation Behavior: A Statistical Evaluation Framework
Recent advancements in deep reinforcement learning have brought forth an...
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11/23/2021
Generating GPU Compiler Heuristics using Reinforcement Learning
GPU compilers are complex software programs with many optimizations spec...
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10/15/2021
Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations
Quantization and pruning are core techniques used to reduce the inferenc...
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07/15/2021
An Energy-Efficient Edge Computing Paradigm for Convolution-based Image Upsampling
A novel energy-efficient edge computing paradigm is proposed for real-ti...
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01/31/2021
Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework
The use of Deep Learning hardware algorithms for embedded applications i...
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01/30/2021
A Competitive Edge: Can FPGAs Beat GPUs at DCNN Inference Acceleration in Resource-Limited Edge Computing Applications?
When trained as generative models, Deep Learning algorithms have shown e...
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10/28/2019
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems
Stochastic-sampling-based Generative Neural Networks, such as Restricted...
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03/11/2019