A new architecture replaces traditional bottlenecks with a passive, single-shot light-speed operation that could become the foundational hardware for AGI, scientists argue. When you purchase through ...

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...

Abstract: Distributed computations, such as distributed matrix multiplication, can be vulnerable to significant security issues, notably Byzantine attacks. These attacks may target either worker nodes ...

TL;DR: Intel and AMD have jointly advanced the x86 instruction set with new standards like FRED, AVX10, ChkTag, and ACE to enhance latency, security, throughput, and compatibility across devices from ...

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The minimal reproducible code is described below. Consider a standard autocast training framework, where a weight matrix is a learnable parameter stored in float type; and input is a sparse_csr ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of computing a matrix inverse using the Newton iteration algorithm. Compared to other algorithms, Newton ...

Google DeepMind’s AI systems have taken big scientific strides in recent years — from predicting the 3D structures of almost every known protein in the universe to forecasting weather more accurately ...

Google’s DeepMind research division claims its newest AI agent marks a significant step toward using the technology to tackle big problems in math and science. The system, known as AlphaEvolve, is ...

Google DeepMind today pulled the curtain back on AlphaEvolve, an artificial-intelligence agent that can invent brand-new computer algorithms — then put them straight to work inside the company's vast ...