Femtosense
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About
Femtosense offers efficient AI solutions focusing on running sparse neural networks on edge devices. Their technology includes a state-of-the-art accelerator - the SPU-001, which is a hyper-efficient hardware solution designed for low power consumption while maintaining performance. Users can develop bespoke neural networks and optimize models using an SDK that integrates widely-used ML frameworks like PyTorch. They provide tools for model performance simulations, optimization techniques, and scalable IP core designs for various applications, including speech enhancement and anomaly detection.
Platform
Task
Features
• ultra-low-latency speech enhancement
• co-processor chip for edge ai
• ai model optimization tools
• performance simulation capabilities
• support for pytorch
FAQs
WHAT DOES FEMTOSENSE OFFER?
We offer efficient AI solutions for running sparse neural networks on edge devices, spanning accelerator hardware to sparse algorithms. Our offerings include bespoke neural networks, model optimization tools, model performance simulators, compilers, scalable IP core designs, co-processor chips, and project advisory.
CAN THE SPU DO COMPUTER VISION?
SPU-001 supports inference on audio and speech. The next iteration, SPU-002, will support vision and NLP tasks and is expected to be available for testing in 2024.
CAN I RUN DENSE MODELS ON THE SPU?
Yes, but a sparse model will give better performance trade-offs. Introducing sparsity marginally affects accuracy if done using our toolkit.
CAN I RUN OFF-THE-SHELF MODELS ON THE SPU?
Yes, you can run if the neural network is quantized to SPU-supported precisions.
DO YOU HAVE OPEN POSITIONS?
Yes, we are always looking for driven, curious, and ambitious people; you can send us your resume.
WHAT'S A "FEMTOSENSE"?
Femtosense technology is inspired by energy-efficient computations based on neural activities in the human brain, specifically at the femtojoule level.
WHAT'S WITH THAT LOGO?
Our logo, a self-connecting neuron inspired by RNNs and the Ouroboros, symbolizes the connection in neural processes.
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