Abstract: Dedicated neural-network inference-processors improve latency and power of the computing devices. They use custom memory hierarchies that take into account the flow of operators present in ...

Abstract: In computing-in-memory (CIM) architecture, it is necessary to reliably adjust the precision according to the specific demands of the application, enabling a tradeoff between high precision ...

Researchers from the University of Edinburgh and NVIDIA developed Dynamic Memory Sparsification (DMS), letting large language models reason deeper while compressing the KV cache up to 8× without ...

The relentless advancement of artificial intelligence (AI) across sectors such as healthcare, the automotive industry, and social media necessitates the development of more efficient hardware ...

The super-exponential growth of data harvested from human input and physical measurements is exceeding our ability to build and power infrastructure to communicate, store and analyze, making our ...