IEEE

Learnable Lookup Table for Neural Network Quantization

Abstract: Neural network quantization aims at reducing bit-widths of weights and activations for memory and computational efficiency. Since a linear quantizer (i.e., round(·) function) cannot well fit ...
IEEE

An Area-Efficient Lookup-Table -Based eDRAM Digital CIM Macro for Neural Network Inference

Digital circuit is a promising approach to implement computing-in-memory (CIM) architecture for data-intensive applications, such as neural network inference. Previous digital CIM implementations have ...
How-To Geek on MSN

The hidden costs of whole-column references in Excel: Learn 3 alternatives to optimize your workbook's performance

Whole-column references in Excel are silent performance killers, often forcing the program to manage a range of over a ...
How-To Geek on MSN

The 4 Excel find and replace tricks I use to save hours

Click "Format" next to the Replace With field, and select the correct number format in the Number tab (in this case, ...
Microsoft

LUT Tensor Core: Lookup Table Enables Efficient Low-Bit LLM Inference Acceleration

As large language model (LLM) inference demands ever-greater resources, there is a rapid growing trend of using low-bit weights to shrink memory usage and boost inference efficiency. However, these ...
Tax Guru

GSTN Advisory on reporting values in Table 3.2 of GSTR-3B with FAQs

1. Table 3.2 of Form GSTR-3B captures the inter-state supplies made to unregistered persons, composition taxpayers, and UIN holders out of the total supplies declared in Table 3.1 & 3.1.1 of GSTR-3B ...