Spatial-temporal information perception is widely used for motion processing in dynamic scenes, but present technology requires relatively huge hardware resource consumption. The attention mechanism ...

Transformer networks, driven by self-attention, are central to large language models. In generative transformers, self-attention uses cache memory to store token projections, avoiding recomputation at ...

A new technical paper titled “Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems” was published by researchers at National ...

Seoul National University researchers have developed an ultra-low-voltage electrochemical organic light-emitting transistor ...

The bleeding edge: In-memory processing is a fascinating concept for a new computer architecture that can compute operations within the system's memory. While hardware accommodating this type of ...

A Nature paper describes an innovative analog in-memory computing (IMC) architecture tailored for the attention mechanism in large language models (LLMs). They want to drastically reduce latency and ...