A flow is presented for synthesizing Tensorflow computation graphs into FPGA accelerators using the open-source high-level synthesis (HLS) tool LegUp. The Tensorflow computation graph is represented translated from an intermediate representation in Tensorflow's Accelerated Linear Algebra (XLA) compiler called High Level Optimizer (HLO). This is translated into LLVM intermediate representation (IR) using a modified version of XLA's CPU backend. These modifications enable users to leverage IP modules for computation-intensive operations. For a simple instance of matrix multiply, using even a naively implemented IP is shown to give a 1.7x speedup over baseline accelerators synthesized from the original CPU backend
International audienceHigh-Level Synthesis (HLS) tools are mature enough to provide efficient code g...
Improving data locality of tensor data structures is a crucial optimization for maximizing the perfo...
Abstract—High-level synthesis (HLS) aims at reducing the time-to-market by providing an automated de...
A flow is presented for synthesizing Tensorflow computation graphs into FPGA accelerators using the ...
High-level synthesis (HLS) has been gaining traction re-cently as a design methodology for FPGAs, wi...
The rate of increase in computing performance has been slowing due to the end of processor frequency...
Embedded system designers can achieve energy and performance benefits by using dedicated hardware ac...
Despite the remarkable improvements in the effectiveness of High Level Synthesis tools for FPGA deve...
In this paper, we introduce a new open source high-level synthesis tool called LegUp that allows sof...
Computational intensive applications such as pattern recognition, and natural language processing, a...
Devices combining a general purpose processor and reconfigurable FPGA fabric in the same package hav...
Abstract—We apply high-level synthesis (HLS) to generate Blokus Duo game-playing hardware for the FP...
It is generally accepted that a custom hardware implementation of a set of computations will provide...
Machine learning algorithms continue to receive significant attention from industry and research. As...
For decades, the computational performance of processors has grown at a faster rate than the availab...
International audienceHigh-Level Synthesis (HLS) tools are mature enough to provide efficient code g...
Improving data locality of tensor data structures is a crucial optimization for maximizing the perfo...
Abstract—High-level synthesis (HLS) aims at reducing the time-to-market by providing an automated de...
A flow is presented for synthesizing Tensorflow computation graphs into FPGA accelerators using the ...
High-level synthesis (HLS) has been gaining traction re-cently as a design methodology for FPGAs, wi...
The rate of increase in computing performance has been slowing due to the end of processor frequency...
Embedded system designers can achieve energy and performance benefits by using dedicated hardware ac...
Despite the remarkable improvements in the effectiveness of High Level Synthesis tools for FPGA deve...
In this paper, we introduce a new open source high-level synthesis tool called LegUp that allows sof...
Computational intensive applications such as pattern recognition, and natural language processing, a...
Devices combining a general purpose processor and reconfigurable FPGA fabric in the same package hav...
Abstract—We apply high-level synthesis (HLS) to generate Blokus Duo game-playing hardware for the FP...
It is generally accepted that a custom hardware implementation of a set of computations will provide...
Machine learning algorithms continue to receive significant attention from industry and research. As...
For decades, the computational performance of processors has grown at a faster rate than the availab...
International audienceHigh-Level Synthesis (HLS) tools are mature enough to provide efficient code g...
Improving data locality of tensor data structures is a crucial optimization for maximizing the perfo...
Abstract—High-level synthesis (HLS) aims at reducing the time-to-market by providing an automated de...