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Found 6 papers, 0 papers with code
Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization
We explore, for the first time, per-layer fine- and coarse-grained pruning, in the same DNN architecture, in addition to low bit-width mixed-precision quantization for weights and activations.
OmniBoost: Boosting Throughput of Heterogeneous Embedded Devices under Multi-DNN Workload
Modern Deep Neural Networks (DNNs) exhibit profound efficiency and accuracy properties.
Energy-efficient DNN Inference on Approximate Accelerators Through Formal Property Exploration
Deep Neural Networks (DNNs) are being heavily utilized in modern applications and are putting energy-constraint devices to the test.
Positive/Negative Approximate Multipliers for DNN Accelerators
In addition, we propose a filter-oriented approximation method to map the weights to the appropriate modes of the approximate multiplier.
Reliability-Aware Quantization for Anti-Aging NPUs
Transistor aging is one of the major concerns that challenges designers in advanced technologies.
Control Variate Approximation for DNN Accelerators
In this work, we introduce a control variate approximation technique for low error approximate Deep Neural Network (DNN) accelerators.
