Ultra low latency deep neural network inference for gravitational waves interferometer: *

Low latency Convolutional Neural Network (CNN) inference research is gaining more and more momentum for tasks such as speech and image classications. This is because CNNs have the ability to surpass human accuracy in classication of images. For improving the measurement setup of gravitational waves, low latency CNNs inference are researched. The...

Cryogenic Hardware Considerations Of Neural Network Decoders For Quantum Error Correction Using Rotated Surface Codes

The quantum bits (qubits) at the core of any quantum computers are so fragile that quantum error correction(QEC) schemes are needed to increase their robustness and enable fault-tolerant quantum algorithms. The surface code is one of the most popular QEC schemes, but it requires the availability of an efficient decoder. While neural networks...

Improving the usability and scalability of FINN, a DNN compiler for FPGAs

FINN is a framework developed by Xilinx Research Labs that compiles Deep Neural Network software descriptions into fast and scalable dataflow architectures for inference acceleration on FPGAs. The dataflow<br/>architectures are network dependent, sized according to the user-defined throughput requirements, and constrained by available resources...

Exploring Feasibility of FPGAs in Implantable Medical Devices

Implantable Medical Devices (IMDs) are deployed in patients to treat a range of medical conditions. Technological advancements have enabled manufacturers to fit IMDs with specialized hardware that accelerates compute-intensive medical therapies next to a software-run host processor. However, mostly hardware acceleration is found in the form of...