Mipsology, the Californian machine learning software specialist, says that its Zebra AI inference accelerator achieved the highest efficiency based on the latest MLPerf inference benchmarking.
Zebra, on a Xilinx Alveo U250, achieved more than 2x higher peak performance efficiency than all other commercial accelerators, says Mipsology.
“We are very proud that our architecture proved to be the most efficient for computing neural networks out of all the existing solutions tested, and in ML Perf’s ‘closed’ category which has the highest requirements,” says Ludovic Larzul, CEO and founder, Mipsology, “we beat behemoths like NVIDIA, Google, AWS, and Alibaba, and extremely well-funded startups like Groq, without having to design a specific chip and by tapping the power of FPGA reprogrammable logic. Perhaps the industry needs to stop over-relying on only increasing peak TOPS. What is the point of huge, expensive silicon with 400+ TOPS if nobody can use the majority of it?”
Silicon Valley-based startup Mipsology announced today that its Zebra AI inference accelerator achieved the highest efficiency based on the MLPerf inference test.
The benchmark, which measures training and inference performance of ML hardware, software, and services, pitted Mipsology’s FPGA-based Zebra AI accelerator against venerable data center GPUs like the Nvidia A100, V100, and T4. Comparisons were also drawn with AWS Inferentia, Groq, Google TPUv3, and other.
