Earlier this year we’ve detailed Esperanto’s first neural processor, the ET-SoC-1. The company’s approach for accelerating AI workloads involved integrating a large number of tiny RISC-V cores capable of performing vector and tensor operations. Recently, the company announced that their 7-nanometer ET-SoC-1 chip has returned from the fab, allowing the company to run real code on those chips and experiment with new applications.
While Esperanto is currently working on a number of use cases and applications with customers, at the recent Samsung Foundry event, the company disclosed their ‘AI-SSD’ concept prototype. At the heart of many large-scale consumer-interfacing systems are recommendation engines. The movies you scroll through on Amazon Videos, Netflix, and Hulu, the houses you are shown on Airbnb and Zillow, the posts you see on Twitter and Facebook, and the products you are shown on Home Depot, Walmart, and Amazon are all powered by those recommendation engines. The accuracy of those results can have a significant impact on our daily life.
A typical recommendation engine recommends ‘items’ such as newsfeeds posts based on the user preference as well as historical interactions. In large-scale production data centers (e.g., Google, Facebook, and Amazon), recommendation engines make up upwards of 80% of all the AI workloads being executed. Additionally, despite processing hundreds of different models, the vast majority of them – up to 80% – are dominated by some flavor of embedding-table lookup operations. One such example is shown below.