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Andrew Canis

Researcher at University of Toronto

Publications -  20
Citations -  2021

Andrew Canis is an academic researcher from University of Toronto. The author has contributed to research in topics: High-level synthesis & Field-programmable gate array. The author has an hindex of 13, co-authored 20 publications receiving 1786 citations.

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Proceedings ArticleDOI

LegUp: high-level synthesis for FPGA-based processor/accelerator systems

TL;DR: A new open source high-level synthesis tool called LegUp that allows software techniques to be used for hardware design and produces hardware solutions of comparable quality to a commercial high- level synthesis tool.
Journal ArticleDOI

A Survey and Evaluation of FPGA High-Level Synthesis Tools

TL;DR: This work uses a first-published methodology to compare one commercial and three academic tools on a common set of C benchmarks, aiming at performing an in-depth evaluation in terms of performance and the use of resources.
Journal ArticleDOI

LegUp: An open-source high-level synthesis tool for FPGA-based processor/accelerator systems

TL;DR: Results show that the tool produces hardware solutions of comparable quality to a commercial high-level synthesis tool, and results demonstrate the ability of the tool to explore the hardware/software codesign space by varying the amount of a program that runs in software versus hardware.

LegUp: An Open Source High-Level Synthesis Tool for FPGA-Based Processor/Accelerator Systems Submission for the Special Issue on Application Specic Processors

TL;DR: LegUp as discussed by the authors is a high-level synthesis tool that allows software techniques to be used for hardware design, which can synthesize most of the C language to hardware, including fixed-sized multi-dimensional arrays, structs, global variables and pointer arithmetic.
Proceedings ArticleDOI

Modulo SDC scheduling with recurrence minimization in high-level synthesis

TL;DR: This work proposes a novel modulo scheduler based on an SDC formulation that includes a backtracking mechanism to properly handle multiple scheduling constraints and still achieve the minimum possible initiation interval and describes how to specifically apply associative expression transformations during modulo scheduling to restructure recurrences in complex loops to enable better scheduling.