How to Overcome the Pain Points of AI/ML Hardware Webinar Registration

How to Overcome the Pain Points of AI/ML Hardware

 

AI/ML hardware faces three common pain points: memory bandwidth, computational throughput and on-chip data movement. Next-generation FPGA technology includes a 2D network on chip, GDDR6 memory interfaces and high performance machine learning processors, which present new capabilities to alleviate these pain points and offer a balance of speed, power and cost.

In this webinar, you will learn:

  • Top trends in data generation
  • Three challenges in processing data with AI/ML hardware solutions
  • How FPGA architectures can overcome data processing challenges
  • Featured example: How to achieve 60 TOPS in Speedster®7t FPGAs

Join the webinar to find out why FPGAs and embedded FPGA (eFPGA) IP are ideal platforms for AI/ML inferencing solutions that provide the flexibility of a GPU while performing at ASIC-like speeds.

 

 

Presented by:

Tom Spencer, Sr. Manager, Product Marketing at Achronix 

Tom Spencer is a seasoned marketing and business development executive with 27 years of experience in the semiconductor industry. While holding positions from start-ups to large fortune 500 companies, he has been responsible for multiple product launches for silicon-based transport and networking solutions in mainstream markets such as Telecommunication, Data Center, Cloud, Storage and Enterprise. He is also well-versed in go-to-market strategy creation and product messaging and positioning.