Quantization requires a large amount of CPU memory. However, the memory required can be reduced by using swap memory. Depending on the GPUs/drivers, there may be a difference in performance, which ...

Researchers at Nvidia have developed a novel approach to train large language models (LLMs) in 4-bit quantized format while maintaining their stability and accuracy at the level of high-precision ...

Abstract: Compute-in-memory (CIM) accelerators have emerged as a promising way for enhancing the energy efficiency of convolutional neural networks (CNNs). Deploying CNNs on CIM platforms generally ...

Abstract: A very important and useful class of digital circuits, counters, useful in many applications of digital systems have been discussed in detail, such as the methods and techniques for using of ...

A digital-to-analog converter (DAC) transforms digital binary data into an analog signal using weighted contributions from each bit. In this FAQ, we discuss the two most commonly known ways of DAC ...

As deep learning models continue to grow, the quantization of machine learning models becomes essential, and the need for effective compression techniques has become increasingly relevant. Low-bit ...

Recent research on the 1-bit Large Language Models (LLMs), such as BitNet b1.58, presents a promising direction for reducing the inference cost of LLMs while maintaining their performance. In this ...

In Part 1 of this two-part binary coded decimal (BCD) extravaganza, we introduced a bunch of bodacious concepts, including the binary (base-2) number system, binary logic, binary computers, and bits, ...

Developed originally to easily display numeric and text data on the cheap and popular 7-segment led displays or "tubes", from 1 to 8 digits, like the "4-Bits LED Digital Tube Module" (and for all the ...