Dense-and-Sparse Quantization LLM权重会有一些异常值,将整体数据的范围拉的很大,对于量化性能来说有很大损失。然而,也存在着这样一个机会:将这些异常值移除,会使量化范围缩小为约10倍,显著提升量化分辨率,使得聚类质点更朝向于灵敏值。 本文将权重矩阵 W 分解为包含有异常值的稀疏矩阵 S 和剩余的稠密矩阵 D。 W=D+S
SqueezeLLM is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving. TLDR: Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization. But a naive method ...
QuantizationPruningMatrix multiplication accelerationConvolutionLSTMIn this paper, we present hardware accelerators created with high-level synthesis techniques for sparse and dense matrix multiplication operations. The cores can operate with different precisions and are designed to be integrated in a ...
(multiplier and adder) and registers for preloaded weights and temporarily latched partial sums and inputs. One should note that 8-bit integer formats are widely used in DNN inference engines due to the prevalence of quantization methods [19]. For systolic arrays, we used 128 × 128 and 256...