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TRACK A: Hardware & Systems: The Future of AI with RISC-V

This presentation, by the RISC-V founder, will highlight how RISC-V and vector compute are gaining momentum with AI and ML and computer vision and how it addresses challenges like managing power consumption, extra data movement, the need for multiple libraries, and issues with generational incompatibility. To solve these obstacles, many of the world’s largest data and device companies are turning to vector processing based on the RISC‑V Vector (RVV) 1.0 ISA.

Chip Design

Enterprise AI

ML at Scale

Novel AI Hardware

Hardware Engineering

Systems Engineering

Strategy

Author:

Krste Asanovic

Co-Founder & Chief Architect

SiFive

Krste is SiFive’s Chief Architect and a Co-Founder. He is also a Professor in the EECS Department at the University of California, Berkeley, where he also serves as Director of the ADEPT Lab. Krste leads the RISC‑V ISA project at Berkeley and is Chairman of the RISC‑V Foundation. He is an ACM Fellow and an IEEE Fellow. Krste received his PhD from UC Berkeley, and a BA in Electrical and Information Sciences from the University of Cambridge.

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TRACK B: Models & Data: Reduce Your Speech Transcription Costs by 90%

Deep neural networks (DNNs), a subset of machine learning (ML), provide a foundation for automating conversational artificial intelligence (CAI) applications. FPGAs provide hardware acceleration enabling high-density and low latency CAI. In this presentation, we will provide an overview of CAI, data center use-cases, describe the traditional compute model and its limitations and show how an ML compute engine integrated into the Achronix FPGA can lead to 90% cost reductions for speech transcription.

Enterprise AI

NLP

Novel AI Hardware

ML at Scale

Data Science

Hardware Engineering

Software Engineering

Systems Engineering

Author:

Salvador Alvarez

Senior Manager, Product Planning

Achronix

Salvador Alvarez is the Senior Manager of Product Planning at Achronix, coordinating the research, development, and launch of new Achronix products and solutions. With over 20 years of experience in product growth, roadmap development, and competitive intelligence and analysis in the semiconductor, automotive, and edge AI industries, Sal Alvarez is a recognized expert in helping customers realize the advantages of edge AI and deep learning technology over legacy cloud AI approaches. Sal holds a B.S. in computer science and electrical engineering from the Massachusetts Institute of Technology.