51 citations · 64 across the 7 of their papers we have counts for
5 papers · 1 filter
A2Q+: Improving Accumulator-Aware Weight Quantization
Ian Colbert, Alessandro Pappalardo, Jakoba Petri-Koenig +1
Quantization techniques commonly reduce the inference costs of neural networks by restricting the precision of weights and activations. Recent studies show that also reducing the p…
A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidance
Ian Colbert, Alessandro Pappalardo, Jakoba Petri-Koenig
We present accumulator-aware quantization (A2Q), a novel weight quantization method designed to train quantized neural networks (QNNs) to avoid overflow when using low-precision ac…
Quantized Neural Networks for Low-Precision Accumulation with Guaranteed Overflow Avoidance
Ian Colbert, Alessandro Pappalardo, Jakoba Petri-Koenig
We introduce a quantization-aware training algorithm that guarantees avoiding numerical overflow when reducing the precision of accumulators during inference. We leverage weight no…
QONNX: Representing Arbitrary-Precision Quantized Neural Networks
Alessandro Pappalardo, Yaman Umuroglu, Michaela Blott +11
We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. We first introduce s…
Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference
Benjamin Hawks, Javier Duarte, Nicholas J. Fraser +3
Efficient machine learning implementations optimized for inference in hardware have wide-ranging benefits, depending on the application, from lower inference latency to higher data…