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Using novel technologies for removing computer memory bottlenecks

According to the website Statista, the U.S. had the largest machine learning market worldwide last year, with a value of more than $21 billion. While machine learning continues to be implemented into ...
InfoQ

Using Machine Learning on Microcontrollers: Decreasing Memory and CPU Usage to Save Power and Cost

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
Electronic Design

Memory-Centric Compute Speeds Searches for Machine-Learning Apps

Compute architectures are changing radically from bringing in memory using CXL on the PCI Express interface to integrating machine-learning and artificial-intelligence (ML/AI) accelerators with ...
VentureBeat

Google’s ‘Nested Learning’ paradigm could solve AI's memory and continual learning problem

Researchers at Google have developed a new AI paradigm aimed at solving one of the biggest limitations in today’s large language models: their inability to learn or update their knowledge after ...
Analytics Insight

From Models to Systems: Moxank Patel on Building Machine Learning That Works at Scale

Moxank Patel, Machine Learning Engineer at Meta, has worked across machine learning, information retrieval, NLP, computer vision, backend engineering and produc ...
Semiconductor Engineering

Developing ReRAM As Next Generation On-Chip Memory For Machine Learning, Image Processing And Other Advanced CPU Applications

In modern CPU device operation, 80% to 90% of energy consumption and timing delays are caused by the movement of data between the CPU and off-chip memory. To alleviate this performance concern, ...