Trends, Challenges, and Solutions in AI/ML Hardware Processing
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
- 3 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
Watch 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.
