Webinar Registration: Intel oneAPI HPC with FPGAs
BittWare On-Demand Webinar Using Intel® oneAPI™ to Achieve High-Performance Compute Acceleration with FPGAs Join BittWare and Intel as we look at oneAPI™ with a focus
Rethinking Inference
New Solutions Give Better Alternatives to GPUs
Deploying AI inference on GPUs provides a well-known performance gain over CPUs, but it’s not always the best option. Advances in AI-focused ASICs and even FPGAs in recent years give more options both for datacenter and edge use cases.
Want to discuss the options, including custom cards and getting started with dev kits? Our technical sales team is ready to connect! Fill the form out and we’ll get back to you.
Advantages for Datacenter
- Need real-time, batch size 1 processing? Our Groq AI-based accelerators are simply a better fit here than GPUs, due to a more streamlined processing flow in the design of the chip.
- Need to scale to large volumes (10s or 100s of nodes)? Moving to larger numbers of GPUs suffers from scaling inefficiencies. Instead, a Groq AI chip-based solution has a near-linear scaling, while keeping low latency and effectively acting as one large compute core versus a network of chips and cores.
Advantages for Edge
- As latency is critical for edge AI, our partner EdgeCortix has created a combination of hardware (both FPGA-based and ASIC) and software to address low-latency inference.
- EdgeCortix chips provide many times higher performance-per-watt over GPUs, including in the sub-10 watt power range.
