I am compiling this memorandum based on related materials to document what I have learned about quantization and deployment ...

Researchers in Naples have developed EDOKS, a transparent image similarity metric that combines Earth Mover's Distance ...

A new open-access cross-domain review maps how the same AI algorithms recur across IoT security, network routing, computer ...

Unbearable lightness of the bit: the method of "packing" neural networks will reduce the load on data centers The news, ...

There's no getting around the fact that I'm running out of storage on my NAS. Unfortunately, right now is the worst time to ...

Understanding how contagion unfolds on large, heterogeneous networks is essential for predicting and controlling spreading ...

A scroll through social media can shape what shoppers notice, want, and buy. Algorithms influence everything from product discovery to impulse purchases and ...

Compute Unit Stalls: When data is not supplied in a timely manner from memory, even high-performance NPU cores are forced to stall while waiting for data, resulting in a substantial drop in overall ...

"Optimization demands understanding hardware constraints at the silicon level," reflects Shaibujan Thankappan Kamalamma, whose career spans video codec work, streaming systems, and enterprise security ...

Google’s TurboQuant is making waves in the AI hardware sector by addressing long-standing challenges in memory usage and processing efficiency. Developed with components like the Quantized ...

Memory prices are falling, and stock prices of memory companies took a hit, following news from Google Research of a breakthrough that will greatly reduce the amount of memory needed for AI processing ...

Google has unveiled TurboQuant, a new AI compression algorithm that can reduce the RAM requirements for large language models by 6x. By optimizing how AI stores data through a method called ...