51 citations · 64 across the 7 of their papers we have counts for
3 papers · 1 filter
Shedding the Bits: Pushing the Boundaries of Quantization with Minifloats on FPGAs
Shivam Aggarwal, Hans Jakob Damsgaard, Alessandro Pappalardo +4
Post-training quantization (PTQ) is a powerful technique for model compression, reducing the numerical precision in neural networks without additional training overhead. Recent wor…
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…