Powerfully efficient AI inference for enterprise and cloud

At the heart of Furiosa RNGD is Tensor Contraction Processor architecture (ISCA 2024), specifically designed for efficient tensor contraction operations.
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The fundamental computation of modern day deep learning is tensor contraction, a higher dimensional generalization of matrix multiplication. However, most commercial deep learning accelerators today incorporate fixed-sized matmul instructions as primitives.

RNGD breaks away from that, unlocking powerful performance and efficiency.

We elevate the programming interface between hardware and software to treat tensor contraction as a single, unified operation.

This fundamental design choice streamlines programming, maximizing parallelism and data reuse, while providing flexibility and reconfigurability of compute and maximizes memory resources based on tensor shapes.

Furiosa Compiler leverages this flexibility and reconfigurability of hardware to select the most optimized tactics, delivering powerful and efficient deep learning acceleration for all scales of deployment.

TCP is the compute architecture underlying Furiosa accelerators. With tensor operation as the first-class citizen, Tensor Contraction Processor (TCP) unlocks unparalleled energy efficiency.