Semiconductor Engineering

Ultra-low-bit LLM Inference Allows AI-PC CPUs And Discrete Client GPUs To Approach High-end ...

A new technical paper titled “Pushing the Envelope of LLM Inference on AI-PC and Intel GPUs” was published by researcher at ...
Forbes

How Mixed-Precision Quantization Could Break AI’s Power Addiction

It turns out the rapid growth of AI has a massive downside: namely, spiraling power consumption, strained infrastructure and runaway environmental damage. It’s clear the status quo won’t cut it ...
Semiconductor Engineering

Balancing Training, Quantization, And Hardware Integration In NPUs

Evolving challenges and strategies in AI/ML model deployment and hardware optimization have a big impact on NPU architectures ...
insideHPC

LeapMind Unveils Efficiera Ultra Low-Power AI Inference Accelerator IP

Today LeapMind announced Efficiera, an ultra-low power AI inference accelerator IP for companies that design ASIC and FPGA circuits, and other related products. Efficiera will enable customers to ...
insideHPC

LeapMind Unveils Efficiera Ultra Low-Power AI Inference Accelerator IP

Today LeapMind announced Efficiera, an ultra-low power AI inference accelerator IP for companies that design ASIC and FPGA circuits, and other related products. Efficiera will enable customers to ...
InfoWorld

What is model quantization? Smaller, faster LLMs

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...
VentureBeat

How Microsoft's next-gen BitNet architecture is turbocharging LLM efficiency

One-bit large language models (LLMs) have emerged as a promising approach to making generative AI more accessible and affordable. By representing model weights with a very limited number of bits, ...