With the power of Deci’s Automated Neural Architecture Construction (AutoNAC) technology, developers are better suited to build, optimize, and deploy more powerful deep learning models using Intel Chips
Deci, the deep learning company harnessing Artificial Intelligence (AI) to build AI, announced a new strategic collaboration with Intel to accelerate the journey toward more scalable AI. By combining Deci’s proprietary Automated Neural Architecture Construction (AutoNAC) technology with Intel chip architectures, the two companies will further optimize deep learning inference, enabling developers everywhere to build, optimize, and deploy more accurately, fast, and fast and efficient models for the edge, data centre, and cloud.
As the Deci-Intel collaboration continues, Deci recently joined the Intel Disruptor Program, which provides participants with technical enablement and go-to-market activities. Deci was also one of the first companies to join Intel Ignite, an accelerator program designed to support innovative startups in advancing new technologies in disruptive markets.
Deci is now working with Intel to demonstrate AutoNAC’s performance on 4th Gen Intel Scalable processors, codenamed Sapphire Rapids. Deci and Intel are making significant steps towards enabling breakthrough deep learning inference on CPUs, a break from tradition as GPUs have generally been the default choice for AI tasks.
“As a result of our collaboration with Intel, we’ve seen exciting achievements in such a short period – deep learning at scale on CPUs is more feasible than ever before,” said Yonatan Geifman, CEO and Co-Founder of Deci. “We expect that our joint activities will only further propel AI accessibility, dramatically optimising deep learning inference for any task in any environment.”
Deci and Intel first announced their broader strategic business and technology collaboration in 2021, following several groundbreaking submissions at MLPerf. In 2022, Deci announced its results for both its Computer Vision (CV) and Natural Language Processing (NLP) models that were submitted to the MLPerf v2.0 Datacenter Open division. On several Intel Architecture (CPUs), Deci’s AutoNAC generated models that delivered breakthrough accuracy and throughput performance- for their CV submission, Deci delivered +1.74% improvement in accuracy and 4x improvement in throughput, while for their NLP submission, Deci improved accuracy by +1.03% and throughput performance by 5x. This was a continuation of their MLPerf results in 2021 where on several Intel CPUs, Deci reduced the submitted models’ latency by a factor of up to 11.8x and increased throughput by up to 11x– all while preserving the model’s accuracy within 1 per cent.