High-level synthesis for FPGAs: From prototyping to deployment

Abstract-Escalating System-on-Chip design complexity is pushing the design community to raise the level of abstraction beyond RTL. Despite the unsuccessful adoptions of early generations of commercial high-level synthesis (HLS) systems, we believe that the tipping point for transitioning to HLS methodology is happening now, especially for FPGA designs. The latest generation of HLS tools has made significant progress in providing wide language coverage and robust compilation technology, platform-based modeling, advancement in core HLS algorithms, and a domain-specific approach. In this paper we use AutoESL's AutoPilot HLS tool coupled with domain-specific system-level implementation platforms developed by Xilinx as an example to demonstrate the effectiveness of state-of-art C-to-FPGA synthesis solutions targeting multiple application domains. Complex industrial designs targeting Xilinx FPGAs are also presented as case studies, including comparison of HLS solutions versus optimized manual designs. Index Terms-Domain-specific design, field-programmable gate array (FPGA), high-level synthesis (HLS), quality of results (QoR)

Bit-Level Optimization for High-Level Synthesis and FPGA-Based Acceleration

Automated hardware design from behavior-level abstraction has drawn wide interest in FPGA-based acceleration and configurable computing research field. However, for many high-level programming languages, such as C/C++, the description of bitwise access and computation is not as direct as hardware description languages, and high-level synthesis of algorithmic descriptions may generate suboptimal implementations for bitwise computation-intensive applications. In this paper we introduce a bit-level transformation and optimization approach to assisting high-level synthesis of algorithmic descriptions. We introduce a bit-flow graph to capture bit-value information. Analysis and optimizing transformations can be performed on this representation, and the optimized results are transformed back to the standard data-flow graphs extended with a few instructions representing bitwise access. This allows high-level synthesis tools to automatically generate circuits with higher quality. Experiments show that our algorithm can reduce slice usage by 29.8 % on average for a set of real-life benchmarks on Xilinx Virtex-4 FPGAs. In the meantime, the clock period is reduced by 13.6 % on average, with an 11.4 % latency reduction

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs

Towards hardware as a reconfigurable, elastic, and specialized service

As modern Data Center workloads become increasingly complex, constrained, and critical, mainstream CPU-centric computing has had ever more difficulty in keeping pace. Future data centers are moving towards a more fluid and heterogeneous model, with computation and communication no longer localized to commodity CPUs and routers. Next generation data-centric Data Centers will compute everywhere, whether data is stationary (e.g. in memory) or on the move (e.g. in network). While deploying FPGAs in NICS, as co-processors, in the router, and in Bump-in-the-Wire configurations is a step towards implementing the data-centric model, it is only part of the overall solution. The other part is actually leveraging this reconfigurable hardware. For this to happen, two problems must be addressed: code generation and deployment generation. By code generation we mean transforming abstract representations of an algorithm into equivalent hardware. Deployment generation refers to the runtime support needed to facilitate the execution of this hardware on an FPGA. Efforts at creating supporting tools in these two areas have thus far provided limited benefits. This is because the efforts are limited in one or more of the following ways: They i) do not provide fundamental solutions to a number of challenges, which makes them useful only to a limited group of (mostly) hardware developers, ii) are constrained in their scope, or iii) are ad hoc, i.e., specific to a single usage context, FPGA vendor, or Data Center configuration. Moreover, efforts in these areas have largely been mutually exclusive, which results in incompatibility across development layers; this requires wrappers to be designed to make interfaces compatible. As a result there is significant complexity and effort required to code and deploy efficient custom hardware for FPGAs; effort that may be orders-of-magnitude greater than for analogous software environments. The goal of this dissertation is to create a framework that enables reconfigurable logic in Data Centers to be targeted with the same level of effort as for a single CPU core. The underlying mechanism to this is a framework, which we refer to as Hardware as a Reconfigurable, Elastic and Specialized Service, or HaaRNESS. In this dissertation, we address two of the core challenges of HaaRNESS: reducing the complexity of code generation by constraining High Level Synthesis (HLS) toolflows, and replacing ad hoc models of deployment generation by generalizing and formalizing what is needed for a hardware Operating System. These parts are unified by the back-end of HLS toolflows which link generated compute pipelines with the operating system, and provide appropriate APIs, wrappers, and software runtimes. The contributions of this dissertation are the following: i) an empirically guided set of systematic transformations for generating high quality HLS code; ii) a framework for instrumenting HLS compiler to identify and remove optimization blockers; iii) a framework for RTL simulation and IP generation of HLS kernels for rapid turnaround; and iv) a framework for generalization and formalization of hardware operating systems to address the {\it ad hoc}'ness of existing deployment generation and ensure uniform structure and APIs