Neurawave
QCi’s first reservoir computing product is a photonic-inspired edge device that is optimized to instantiate recurrent neural networks. This device can be used to solve a variety of problems related to serial data structures including time series prediction, image recognition, and text classification. It is fast, affordable, energy-efficient, and brings the power of a standalone edge computing to your desktop.
Capabilities and applications
Our electro-optical analog machine learning hardware is built for AI on the edge:
Classification
Prediction Problems
Function Fitting
Specifications
Form Factor | PCIe 3.0 form factor with x4 PCIe lanes, 3 slots of PCIE |
ADC Sampling Rate | 1.25 GSps with a 14-bit resolution |
DAC Sampling Rate | 1.25 GSps with a 16-bit resolution |
Max Nodes | 10,000 (1.6 km fiber in 1st prototype) |
Throughput | 2.5 GB/s over PCIe |
Power Consumption | ~36 W |
Dimensions (L x W x H) | 390 mm x 130 mm x 55.5 mm |
Weight | 1.87 kg |