The COVID 19 pandemic drove rapid positive innovation across both healthcare and life sciences. The urgent need to take patient care “virtual” drove a 5-year acceleration in telehealth in only a few months. The race to find a vaccine required pharma companies to do drug discovery in new, better and faster ways than ever before. AI and machine learning were an important part of the success of these efforts – and no one wants to go back to the old ways. Practical applications of NLP, computer vision and recommendation models will continue to support better outcomes for patients and accelerate new drug discovery in a post pandemic AI driven ecosystem.

The demands of ever more sophisticated Artificial Intelligence have pushed classical compute hardware and software to its limits. But will the architectures being put in place to enable the next generation of AI also transform how we approach traditional HPC and general computing?

That is precisely what software-defined AI hardware startup SambaNova Systems is working towards with its Reconfigurable Dataflow Architecture (RDA), which reimagines how we can free AI from the constraints of traditional software and hardware.

This extended profile explains how SambaNova Systems’ founders have drawn on decades of experience at some of Silicon Valley’s most storied companies and institutions to pinpoint why traditional architectures are running out of road when it comes to advancing machine learning and AI, and how this led to the development of RDA.