Access the NPS through the Q.ANT Photonic Algorithms Library (Q.PAL), C/C++ and Python APIs, and frameworks like PyTorch.
Q.ANT software turns photonic computing into real applications. On a platform powered by light, complex mathematical models run with exceptional performance. From AI training and inference and machine learning to physics simulations and time-series analysis. The Q.ANT Photonic Algorithms Library (Q.PAL) is your interface to the NPU: with a comprehensive collection of example applications to build on. Because photonic processors are the natural fit for large-scale nonlinear networks, your models need fewer parameters and achieve higher.
Q.ANT’s Native Processing Server executes demanding AI workloads directly in light. While CMOS processors excel at linear, sequential processing, photonic processors are the natural hardware fit for large-scale nonlinear algorithms. Networks with nonlinear functions reduce the number of model parameters needed allowing higher accuracy per parameter and training budget. In photonic processors one single optical element performs one nonlinear operation while CMOS requires 100-1000 transistors and multiple cycles.
In this example, a network using learnable nonlinear functions on Q.ANT‘s NPS reconstructs complex image patterns more accurately than a linear network on a CPU while 2x less parameters and 3x less operations are needed.
# parameters: ~20k
# operations: ~670m
# parameters: ~10k
# operations: ~250m
Photonic Computing enables faster execution of matrix operations and non-linear functions directly in hardware. This allows for more efficient model architectures with fewer parameters. The result: higher throughput and lower power consumption for both, training large-scale models and real-time inference.
Many image processing tasks are inherently mathematical relying heavily on transforms like Fourier or convolution operations. With photonic processors, these operations can be performed optically, in parallel and at the speed of light. This dramatically increases frame rates and lowers energy usage.
Scientific simulations often depend on solving complex partial differential equations and large-scale matrix systems. Photonic hardware provides a powerful platform for these workloads by enabling high-bandwidth, computing that scales with problem complexity, helping simulate physical phenomena faster and with greater efficiency.
The Q.ANT Photonic Algorithms Library (Q.PAL) is the software interface to the NPS which enables users to operate directly at the multiplication level or to leverage optimized neural network operations such as fully connected layers or convolutional layers. Q.PAL offers a comprehensive collection of example applications that illustrate how AI applications can be enhanced. These examples can be implemented directly or as a foundation for creating custom use cases.
| Name | Description | Programming Language |
| Matrix Multiplication | Multiplication of a matrix and a vector | Python / C++ |
| Image Classification | Classification of an image (e.g. based on the ImageNet data set) | Python (Jupyter) |
| Semantic Segmentation | Segmentation of an image (e.g. based on a brain MRI scan data set) | Python (Jupyter) |
| Complex Line Fitting | Fitting of a high frequency line with a nonlinear network (e.g. based on simulated training data) | Python (Jupyter) |
Q.ANT’s photonic processing solution seamlessly integrates into the existing compute landscape. The Native Processing Unit at the heart of the NPS provides a PCIe interface housed in a standard 19” server, which makes the system plug-and-play. The NPU can be accessed via a software interface with C/C++ and Python APIs and will integrate into common AI frameworks such as PyTorch. Q.ANT supports customers in creating custom applications, providing the Q.ANT Photonic Algorithms Library and training resources.
VP Software
I look forward to discussing the potentials of Photonic Computing with you.
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