Nature

Physical unclonable in-memory computing for simultaneous protecting private data and deep learning models

Compute-in-memory based on resistive random-access memory has emerged as a promising technology for accelerating neural networks on edge devices. It can reduce frequent data transfers and improve ...
Nasdaq

Compute-In-Memory APU Achieves GPU-Class AI Performance at a Fraction of the Energy Cost

The researchers’ findings point to significant opportunities for GSI Technology as customers increasingly require performance-per-watt gains across various industries, including Edge AI for ...
EDN

Analog in-memory compute tackles the AI inference conundrum

An analog in-memory compute chip claims to solve the power/performance conundrum facing artificial intelligence (AI) inference applications by facilitating energy efficiency and cost reductions ...
Hosted on MSN

Anker THUS chip breaks computing rules to put big AI models on wearable devices

Anker is getting into the silicon business, specifically building a CIM (Compute In Memory) solution that will support onboard large model processing inside tiny, low-powered Bluetooth earbuds. THUS ...
Business Wire

d-Matrix Unveils Corsair, the World’s Most Efficient AI Computing Platform for Inference in Datacenters

ATLANTA--(BUSINESS WIRE)--d-Matrix today officially launched Corsair™, an entirely new computing paradigm designed from the ground-up for the next era of AI inference in modern datacenters. Corsair ...
Semiconductor Engineering

PDN Challenges In DRAM-Based Compute-In-Memory Systems (UT Austin)

A new technical paper, “A comparative study on power delivery aspects of compute-in/near-memory approaches using DRAM,” was published by researchers at UT Austin. “Compute-in-memory (PIM) mitigates ...
Nature

Analog in-memory computing attention mechanism for fast and energy-efficient large language models

Transformer networks, driven by self-attention, are central to large language models. In generative transformers, self-attention uses cache memory to store token projections, avoiding recomputation at ...
Semiconductor Engineering

Memory Wall Problem Grows With LLMs

The growing imbalance between the amount of data that needs to be processed to train large language models (LLMs) and the inability to move that data back and forth fast enough between memories and ...
VentureBeat

Meta proposes new scalable memory layers that improve knowledge, reduce hallucinations

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more As enterprises continue to adopt large ...
NextBigFuture

Analog in-memory Computing Attention Mechanism for Fast and Energy-efficient Large Language Models

A Nature paper describes an innovative analog in-memory computing (IMC) architecture tailored for the attention mechanism in large language models (LLMs). They want to drastically reduce latency and ...