Fast Matrix Multiplication for AI

Matrix multiplication consumes huge amount of resources: computing time and energy, primarily in AI applications.

The industry has recognized the need for faster and more energy-efficient matrix multiplication with state-of-the-art solutions in software (e.g., DGEMM of Intel's math kernel library (MKL) for CPU and NVIDIA's CUDA for GPU) and hardware (e.g., Google's TPU and Intel / Habana labs Gaudi accelerator).

Unfortunately, all present solutions employ a wasteful cubic-time algorithm. We have developed methods that provide speedup for matrix multiplication in SW and in HW. The novel developments of Prof. Oded Schwartz and his strong team are based on years of research, and are protected by several patents.

The funds are requested to pursue business opportunity.