Low-Power High-Level Synthesis for FPGA Architectures

This paper addresses two aspects of low-power design for FPGA circuits. First, we present an RT-level power estimator for FPGAs with consideration of wire length. The power estimator closely reflects both dynamic and static power contributed by various FPGA components in 0.1um technology. The power estimation error is 16.2% on average. Second, we present a low power high level synthesis system, named LOPASS, for FPGA designs. It includes two algorithms for power consumption reduction: (i) a simulated annealing engine that carries out resource selection, function unit binding, scheduling, register binding, and data path generation simultaneously to effectively reduce power; (ii) an enhanced weighted bipartite matching algorithm that is able to reduce the total amount of MUX ports by 22.7%. Experimental results show that LOPASS is able to reduce power consumption by 35.8% compared to the results of Synopsys' Behavioral Compiler.

We analyze memoization-based approximate computing with FPGA as the computing substrate from all of the above perspectives. We present and discuss the static memoization and dynamic memoization as well as the comparison measure settings for the applications to benefit from these specific memoization techniques. The nature and the availability of data sets are analyzed, which determines the proper memoization method for a better quality of results. An integrated high-level synthesis (HLS) flow is developed to automatically synthesize the memoization-based design. Based on our experiments using both the image-processing applications and common computation kernels with their data sets, we make recommendations regarding selection of proper memoization techniques and comparison measures. The experimental results show a significant power saving (around 20%) with small quality degradation.

FPGA High Level Synthesis for Low Power ..

AB - This paper addresses two aspects of low-power design for FPGA circuits. First, we present an RT-level power estimator for FPGAs with consideration of wire length. The power estimator closely reflects both dynamic and static power contributed by various FPGA components in 0.1um technology. The power estimation error is 16.2% on average. Second, we present a low power high level synthesis system, named LOPASS, for FPGA designs. It includes two algorithms for power consumption reduction: (i) a simulated annealing engine that carries out resource selection, function unit binding, scheduling, register binding, and data path generation simultaneously to effectively reduce power; (ii) an enhanced weighted bipartite matching algorithm that is able to reduce the total amount of MUX ports by 22.7%. Experimental results show that LOPASS is able to reduce power consumption by 35.8% compared to the results of Synopsys' Behavioral Compiler.

N2 - This paper addresses two aspects of low-power design for FPGA circuits. First, we present an RT-level power estimator for FPGAs with consideration of wire length. The power estimator closely reflects both dynamic and static power contributed by various FPGA components in 0.1um technology. The power estimation error is 16.2% on average. Second, we present a low power high level synthesis system, named LOPASS, for FPGA designs. It includes two algorithms for power consumption reduction: (i) a simulated annealing engine that carries out resource selection, function unit binding, scheduling, register binding, and data path generation simultaneously to effectively reduce power; (ii) an enhanced weighted bipartite matching algorithm that is able to reduce the total amount of MUX ports by 22.7%. Experimental results show that LOPASS is able to reduce power consumption by 35.8% compared to the results of Synopsys' Behavioral Compiler.

High-level synthesis for FPGA power minimization; (a) ..

We assume an iterative design flow for memoization based approximate computing. The details of this iterative design flow are shown in Fig. 2. Here, P1 and P2 refer to the power values obtained without memoization and with memoization, respectively;R1 and R2 refer to the computed values obtained without memoization and with memoization, respectively; P and T are the power and result accuracy thresholds, respectively. Fig. 3 shows not only the memoization architecture generation flow but also the considerations related to power and accuracy of results that must be considered. The red block shows that an application or task described in C/C++language is synthesized using an HLS tool. The blue block shows memoization architecture generator, which generates the RTL wrapper module to wrap the HLS synthesized block with memoization related circuit blocks. As a result of this wrapping, the RTL design of memoized architecture is generated (purple block), i.e., the top-level module which contains the RTL wrapper and the HLS synthesized block. After placement and routing on target FPGA using a vendor specific placement and routing tool (such as Xilinx ISE), the simulation-based dynamic power analysis of both the HLS synthesized design and the memoized architecture is performed separately to evaluate the potential power saving (green block). The power analysis is performed using the data set corresponding to the application (strong or weak). The percentage difference between P1 and P2 should be greater than user-defined threshold P compared with the area overhead due to the wrapper.

Field-programmable gate arrays (FPGAs) are increasingly used as the computing platform for fast and energy efficient execution of recognition, mining, and search applications. Approximate computing is one promising method for achieving energy efficiency. Compared with most prior works on approximate computing, which target approximate processors and arithmetic blocks, this paper presents an approximate computing methodology for FPGA-based design. It studies memoization as a method for approximation on FPGA and analyzes different architectural and design parameters that should be considered. The proposed design flow leverages on high-level synthesis to enable memoization-based microarchitecture generation, thus also facilitating a C-to-register-transfer-level synthesis. When compared with the previous approaches of bit-width truncation and approximate multipliers, memoization-based approximate computation on FPGA achieves a significant dynamic power saving (around 20%) with very small area overhead (

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-Applications and Design Studies: Implementation of novel designs on FPGAs to achieve high performance, low power, or high reliability, making use of the unique flexibility provided by FPGA architectures.

High-level synthesis - Wikipedia

Chen recently received a three-year $300,000 grant from to continue research on customized polyhedral compilation for low-power high-level system-on-a-chip (SoC) synthesis. Pouchet of UCLA joins the effort as a subcontract researcher for this project.