Accuracy of Digital, Efficiency of Analog

Meet the industry's newest architecture for AI computing on-the-device and at-the-edge. DigAn by Ambient Scientific is
the foundational technology suite for a new generation of AI at the edge.

AI

    DigAn is designed for AI

  • L1000x lower power consumption (~8 µw per core) than ARM for inference
  • 250 GOPS with 10 AI cores for more processing than X86
  • Scalable with CMOS process for low-cost manufacturing
  • Programmable accuracy: 4-bit to 32-bit for power optimization
On-Device Inference

    On-device inference and training

  • Architecture built for neural networks to run real-time inference and on-board training
DigAn™

    DigAn is designed for AI

  • L1000x lower power consumption (~8 µw per core) than ARM for inference
  • 250 GOPS with 10 AI cores for more processing than X86
  • Scalable with CMOS process for low-cost manufacturing
  • Programmable accuracy: 4-bit to 32-bit for power optimization

Analog Matrix Computing

The DigAn architecture is designed to accelerate neural network models for AI inference. Each neuron is defined at the hardware level. This gives the DigAn architecture its speed and power efficiency. DigAn enables our GPX processors to perform inference and training on the device so models aren’t stored in a data center, and inference doesn’t occur in the cloud. DigAn is on-device AI.

Analog Matrix Computing

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