That’s why at QCi we build super-AI systems using hybrid photonic-electronic architectures where photons and electrons work together to push the computing boundaries needed for AI & ML. Photonic computing can be extremely fast and energy efficient, and inherently supports parallel processing at large scale. Yet it is challenging to prepare, interact, and store many photonic signals. Conversely, electronic signals are easy to create and manipulate, but their processing speed and parallelism are heavily capped.
Photonic analog computing for directed AI
What they can doOur photonic analog computer are purpose built for directed AI.
How it worksOur reservoir computers map input signals into higher dimensional computational spaces through the dynamics of a fixed, non-linear system called a reservoir. After the input signal is fed into the reservoir, which is treated as a "black box," a simple readout mechanism is trained to read the state of the reservoir and convert it to the desired output.
Dive a little deeper into how reservoir computing systems work. To see our research and publications, click here.
All products are rooted in our scientific publications. To see an exhaustive list of our publications, click here.
Efficient optical reservoir computing for parallel data processing
Efficient reservoir computing using field programmable gate array and electro-optic modulation
Single-pixel pattern recognition with coherent nonlinear optics
SummaryStep into unparalleled computation with our high-speed photonic reservoir computer, elegantly housed in a sleek 2U server form factor. At its core is the opto-electronic time delay feedback reservoir, delivering a remarkable 1.25G nodes per second and boasting the capacity for up to 60,000 virtual nodes. This compact powerhouse is tailor-made for intricate time series analysis, proficiently managing everything from real-time data streams to sophisticated temporal pattern recognition. In an era defined by data, our system stands as the embodiment of precision and reliability, setting the benchmark for processing excellence.
|530mm x 430mm x 88.6mm
|Typical Power Consumption
|10/100/1000 wired ethernet
|Transient Response Rate
|Number of Internal Nodes:
|up to 60,000 internal nodes
SummaryQCi’s first reservoir computing product is an edge device that is photonic-inspired, FPGA based, and optimized for recurrent neural network applications. This device has can be applied to solve a variety of problems related to serial data structures including time series prediction, image recognition, and text classification. It’s fast, affordable, energy efficient – and brings the power of a standalone edge computing solution to your desktop.
|170 mm x 90 mm x 55 mm
|USB-C input power control with 45W power adaptor
Typical Power Consumption
|0°C to 70°C
|10/100/1000 wired ethernet
Transient Response Rate
Number of Internal Nodes
|up to 8,192 internal nodes