A one-shot training framework uses dynamic BN zeroization/recovery and an ML-based latency predictor to compress or scale DNNs to meet hard latency constraints on edge devices.
Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
Deep learning algorithms achieve high classification accuracy at the expense of significant computation cost. In order to reduce this cost, several quantization schemes have gained attention recently with some focusing on weight quantization, and others focusing on quantizing activations. This paper proposes novel techniques that target weight and activation quantizations separately resulting in an overall quantized neural network (QNN). The activation quantization technique, PArameterized Clipping acTivation (PACT), uses an activation clipping parameter $\alpha$ that is optimized during training to find the right quantization scale. The weight quantization scheme, statistics-aware weight binning (SAWB), finds the optimal scaling factor that minimizes the quantization error based on the statistical characteristics of the distribution of weights without the need for an exhaustive search. The combination of PACT and SAWB results in a 2-bit QNN that achieves state-of-the-art classification accuracy (comparable to full precision networks) across a range of popular models and datasets.
years
2026 2representative citing papers
A statistical error analysis framework yields iterative and analytic quantizers that improve accuracy and stability when incorporated into quantization-aware training for integer and floating-point formats.
citing papers explorer
-
Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization
A one-shot training framework uses dynamic BN zeroization/recovery and an ML-based latency predictor to compress or scale DNNs to meet hard latency constraints on edge devices.
-
StatQAT: Statistical Quantizer Optimization for Deep Networks
A statistical error analysis framework yields iterative and analytic quantizers that improve accuracy and stability when incorporated into quantization-aware training for integer and floating-point formats.