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Andes Relies on Deeplite

In Deploying Highly Compact Deep Learning Models

Deep Neural Networks deliver state-of-the-art accuracy on many AI tasks, but they come at a cost. They are large, process-heavy, and consume too much power to be installed on devices. This computation overhead limits the applicability of DNNs to low-power, embedded platforms.

Learn how Andes and Deeplite worked together on a person detection use case, using low-power RISC-V MCU DSP (<256KB SRAM). Deeplite result was 2.7% more accurate and 15% faster than TensorFlow Lite Micro!

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