Next-Generation Low-Bit Computing for Energy-Efficient AI
Next-Generation Low-Bit Computing for Energy-Efficient AI
Delivering ultra-efficient AI compute with zero accuracy loss
Delivering ultra-efficient AI compute with zero accuracy loss
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Delivering ultra-efficient AI compute with zero accuracy loss
Deploying real-world ML models on today’s INT-based Neural Processing Unit (NPU) is unnecessarily complex due to post training quantization process (PTQ), which often leads to AI accuracy loss, and instability in output.
An innovative number system designed for encoding-aware training from the outset — enabling low bit computing without modifying network structure, and without sacrificing FP32-level accuracy of neural network.
Asymmetric number encoding system for machine learning
Flexible NPU design framework with 2200x improvement in PPA
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