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...

Optimizing Memory Mapping for Dataflow Inference Accelerators: Efficient Memory Utilization on FPGAs

Convolutional Neural Network (CNN) inference has gained a significant amount of traction for performing tasks like speech recognition and image classification. To improve the accuracy with which these tasks can be performed, CNNs are typically designed to be deep, encompassing a large number of neural network layers. As a result, the...

The Case for Polymorphic Registers in Dataflow Computing

Heterogeneous systems are becoming increasingly popular, delivering high performance through hardware specialization. However, sequential data accesses may have a negative impact on performance. Data parallel solutions such as Polymorphic Register Files (PRFs) can potentially accelerate applications by facilitating high-speed, parallel access to...

Solving Poisson's equation with dataflow computing

The finite element method (FEM) is an ubiquitous method for the analysis of boundary value problems. Specifically, it can be used to find approximations to solutions of boundary value problems on a specific domain...