The Ideal Solution for AI Applications - Speedcore eFPGA
By Achronix Semiconductor Corporation
Introduction and Background
Artificial intelligence (AI) is reshaping the way the world works, opening up countless opportunities in commercial and industrial systems. Applications span diverse markets such as autonomous driving, medical diagnostics, home appliances, industrial automation, adaptive websites and financial analytics. Even the communications infrastructure linking these systems together is moving towards automated self-repair and optimization. These new architectures will perform functions such as load balancing and allocating resources such as wireless channels and network ports based on predictions learned from experience.
These applications demand high performance and, in many cases, low latency to respond successfully to realtime changes in conditions and demands. They also require power consumption to be as low as possible, rendering unusable, solutions that underpin machine-learning in cloud servers where power and cooling are plentiful. A further requirement is for these embedded systems to be always on and ready to respond even in the absence of a network connection to the cloud. This combination of factors calls for a change in the way that hardware is designed.
Related Semiconductor IP
- eFPGA
- eFPGA on GlobalFoundries GF12LP
- Heterogeneous eFPGA architecture with LUTs, DSPs, and BRAMs on GlobalFoundries GF12LP
- eFPGA Soft IP
- Radiation-Hardened eFPGA
Related White Papers
- PCIe 5.0: The universal high-speed interconnect for High Bandwidth and Low Latency Applications Design Challenges & Solutions
- Boosting RISC-V SoC performance for AI and ML applications
- Seven Powerful Reasons Why Menta eFPGA Is the Clear Choice for A&D ASICs
- Breaking the HBM Bit Cost Barrier: Domain-Specific ECC for AI Inference Infrastructure
Latest White Papers
- Attack on a PUF-based Secure Binary Neural Network
- BBOPlace-Bench: Benchmarking Black-Box Optimization for Chip Placement
- FD-SOI: A Cyber-Resilient Substrate Against Laser Fault Injection—The Future Platform for Secure Automotive Electronics
- In-DRAM True Random Number Generation Using Simultaneous Multiple-Row Activation: An Experimental Study of Real DRAM Chips
- SPAD: Specialized Prefill and Decode Hardware for Disaggregated LLM Inference