Breaking through the memory and bandwidth bottlenecks of traditional control-centric architectures with dataflow computing.

Significantly boosting data processing parallelism and energy efficiency by adopting a fully digital compute-in-memory (CIM) approach.

Enabling full-scenario large model inference deployment at the edge.

Leveraging advanced CMOS technology and system-level integration to balance high performance with energy efficiency.

Adapting flexibly to diversified inference scenarios across the cloud-edge-device spectrum.

Enabling future AI applications with high energy-efficient and low-latency computing solutions.