Jai Hyun Park, CryptoLab Inc. Abstract. Matrix multiplication of two encrypted matrices (CC-MM) is a key challenge for privacy-preserving machine learning applications. As modern machine learning ...

If Intel hopes to survive the next few years as a freestanding company and return to its role as innovator, it can not afford to waste its time and it cannot afford to make any more mistakes. Which is ...

Nvidia is the biggest beneficiary of the AI infrastructure buildout. Broadcom has been gaining a lot of traction recently with custom AI chips. Right now, Nvidia has higher growth and is the cheaper ...

There are central processing units (CPUs), graphics processing units (GPUs) and even data processing units (DPUs) – all of which are well-known and commonplace now. GPUs in particular have seen a ...

Since homomorphic encryption enables SIMD operations by packing multiple values into a vector of operations and enabling pairwise addition or multiplication operations, one (old) conventional method ...

A new technical paper titled “Scalable MatMul-free Language Modeling” was published by UC Santa Cruz, Soochow University, UC Davis, and LuxiTech. “Matrix multiplication (MatMul) typically dominates ...

Presenting an algorithm that solves linear systems with sparse coefficient matrices asymptotically faster than matrix multiplication for any ω > 2. Our algorithm can be viewed as an efficient, ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Matrix multiplications (MatMul) are the ...

It has been more than a decade since Google figured out that it needed to control its own hardware fate when it came to the tensor processing that was going to be required to support machine learning ...

Computer scientists have discovered a new way to multiply large matrices faster than ever before by eliminating a previously unknown inefficiency, reports Quanta Magazine. This could eventually ...