A lot has changed in a few years with AI/ML models and inference. The hardware that’s great at crunching training algorithms can fall behind in latency and utilization for real-time, batch size 1 inference. Deep learning models have gotten more complex, requiring new approaches for real-time applications to keep up.
The good new is, as machine learning has matured, acceleration technology has gotten smarter—and more efficient. These are both on a silicon level, such as using dedicated ASIC devices, and in a design approach—like using 8-bit integers for connections.
BittWare, a brand that’s been trusted for over thirty years to bring the best acceleration technology to market, has assembled an ecosystem FPGA- and ASIC-based AI solutions that are optimized for inference.
Whether it’s scaling up a CPU- or GPU-based system to the latest datacenter-grade tensor processor or getting every last watt of performance using an edge-focused solution, we have what you need to reduce risk and get to market faster.
Where do BittWare AI/ML customers deploy?
For some partners we are also supplying development platforms used to evaluate their technology
Our Partner Program ecosystem includes a range of AI/ML inference options, from next-gen ASIC-based cards to IP for development on FPGAs.
We look at the inference of neural networks on FPGA devices, illustrating their strengths and weaknesses.
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