1 citations · 3 across the 4 of their papers we have counts for
5 papers
Accumulator-Aware Post-Training Quantization for Large Language Models
Ian Colbert, Giuseppe Franco, Fabian Grob +2
When quantizing weights and activations to increasingly narrower representations, the cost of additions begins to dominate that of multiplications in multiply-accumulate (MAC) unit…
SPFQ: A Stochastic Algorithm and Its Error Analysis for Neural Network Quantization
Jinjie Zhang, Rayan Saab
Quantization is a widely used compression method that effectively reduces redundancies in over-parameterized neural networks. However, existing quantization techniques for deep neu…
Post-training Quantization for Neural Networks with Provable Guarantees
Jinjie Zhang, Yixuan Zhou, Rayan Saab
While neural networks have been remarkably successful in a wide array of applications, implementing them in resource-constrained hardware remains an area of intense research. By re…
Sigma-Delta and Distributed Noise-Shaping Quantization Methods for Random Fourier Features
Jinjie Zhang, Harish Kannan, Alexander Cloninger +1
We propose the use of low bit-depth Sigma-Delta and distributed noise-shaping methods for quantizing the Random Fourier features (RFFs) associated with shift-invariant kernels. We…
Faster Binary Embeddings for Preserving Euclidean Distances
Jinjie Zhang, Rayan Saab
We propose a fast, distance-preserving, binary embedding algorithm to transform a high-dimensional dataset into binary sequences in the cube $\{\…