The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Katherine Compton - One of the best experts on this subject based on the ideXlab platform.
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scientific Application Acceleration with reconfigurable functional units
Field-Programmable Custom Computing Machines, 2007Co-Authors: Kyle Rupnow, Keith D Underwood, Katherine ComptonAbstract:While scientific Applications in the past were limited by floating point computations, modern scientific Applications use more unstructured formulations. These Applications have a significant percentage of integer computation - increasingly a limiting factor in scientific Application performance. In real scientific Applications employed at Sandia National Labs, integer computations constitute on average 37% of the Application operations, forming large and complex dataflow graphs. Reconfigurable functional units (RFUs) are a particularly attractive accelerator for these graphs because they can potentially accelerate many unique graphs with a small amount of additional hardware. In this study, we analyze Application traces of Sandia's scientific Applications and the SPEC-FP benchmark suite. First we select a set of dataflow graphs to accelerate using the RFU, then we use execution-based simulation to determine the Acceleration potential of the Applications when using an RFU. On average, a set of 32 or fewer graphs is sufficient to capture the dataflow behavior of 30% of the integer computation, and more than half of Sandia Applications show an improvement of 5% or more.
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FCCM - Scientific Application Acceleration with Reconfigurable Functional Units
15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM 2007), 2007Co-Authors: Kyle Rupnow, Keith D Underwood, Katherine ComptonAbstract:While scientific Applications in the past were limited by floating point computations, modern scientific Applications use more unstructured formulations. These Applications have a significant percentage of integer computation - increasingly a limiting factor in scientific Application performance. In real scientific Applications employed at Sandia National Labs, integer computations constitute on average 37% of the Application operations, forming large and complex dataflow graphs. Reconfigurable functional units (RFUs) are a particularly attractive accelerator for these graphs because they can potentially accelerate many unique graphs with a small amount of additional hardware. In this study, we analyze Application traces of Sandia's scientific Applications and the SPEC-FP benchmark suite. First we select a set of dataflow graphs to accelerate using the RFU, then we use execution-based simulation to determine the Acceleration potential of the Applications when using an RFU. On average, a set of 32 or fewer graphs is sufficient to capture the dataflow behavior of 30% of the integer computation, and more than half of Sandia Applications show an improvement of 5% or more.
Kyle Rupnow - One of the best experts on this subject based on the ideXlab platform.
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scientific Application Acceleration with reconfigurable functional units
Field-Programmable Custom Computing Machines, 2007Co-Authors: Kyle Rupnow, Keith D Underwood, Katherine ComptonAbstract:While scientific Applications in the past were limited by floating point computations, modern scientific Applications use more unstructured formulations. These Applications have a significant percentage of integer computation - increasingly a limiting factor in scientific Application performance. In real scientific Applications employed at Sandia National Labs, integer computations constitute on average 37% of the Application operations, forming large and complex dataflow graphs. Reconfigurable functional units (RFUs) are a particularly attractive accelerator for these graphs because they can potentially accelerate many unique graphs with a small amount of additional hardware. In this study, we analyze Application traces of Sandia's scientific Applications and the SPEC-FP benchmark suite. First we select a set of dataflow graphs to accelerate using the RFU, then we use execution-based simulation to determine the Acceleration potential of the Applications when using an RFU. On average, a set of 32 or fewer graphs is sufficient to capture the dataflow behavior of 30% of the integer computation, and more than half of Sandia Applications show an improvement of 5% or more.
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FCCM - Scientific Application Acceleration with Reconfigurable Functional Units
15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM 2007), 2007Co-Authors: Kyle Rupnow, Keith D Underwood, Katherine ComptonAbstract:While scientific Applications in the past were limited by floating point computations, modern scientific Applications use more unstructured formulations. These Applications have a significant percentage of integer computation - increasingly a limiting factor in scientific Application performance. In real scientific Applications employed at Sandia National Labs, integer computations constitute on average 37% of the Application operations, forming large and complex dataflow graphs. Reconfigurable functional units (RFUs) are a particularly attractive accelerator for these graphs because they can potentially accelerate many unique graphs with a small amount of additional hardware. In this study, we analyze Application traces of Sandia's scientific Applications and the SPEC-FP benchmark suite. First we select a set of dataflow graphs to accelerate using the RFU, then we use execution-based simulation to determine the Acceleration potential of the Applications when using an RFU. On average, a set of 32 or fewer graphs is sufficient to capture the dataflow behavior of 30% of the integer computation, and more than half of Sandia Applications show an improvement of 5% or more.
Keith D Underwood - One of the best experts on this subject based on the ideXlab platform.
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scientific Application Acceleration with reconfigurable functional units
Field-Programmable Custom Computing Machines, 2007Co-Authors: Kyle Rupnow, Keith D Underwood, Katherine ComptonAbstract:While scientific Applications in the past were limited by floating point computations, modern scientific Applications use more unstructured formulations. These Applications have a significant percentage of integer computation - increasingly a limiting factor in scientific Application performance. In real scientific Applications employed at Sandia National Labs, integer computations constitute on average 37% of the Application operations, forming large and complex dataflow graphs. Reconfigurable functional units (RFUs) are a particularly attractive accelerator for these graphs because they can potentially accelerate many unique graphs with a small amount of additional hardware. In this study, we analyze Application traces of Sandia's scientific Applications and the SPEC-FP benchmark suite. First we select a set of dataflow graphs to accelerate using the RFU, then we use execution-based simulation to determine the Acceleration potential of the Applications when using an RFU. On average, a set of 32 or fewer graphs is sufficient to capture the dataflow behavior of 30% of the integer computation, and more than half of Sandia Applications show an improvement of 5% or more.
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FCCM - Scientific Application Acceleration with Reconfigurable Functional Units
15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM 2007), 2007Co-Authors: Kyle Rupnow, Keith D Underwood, Katherine ComptonAbstract:While scientific Applications in the past were limited by floating point computations, modern scientific Applications use more unstructured formulations. These Applications have a significant percentage of integer computation - increasingly a limiting factor in scientific Application performance. In real scientific Applications employed at Sandia National Labs, integer computations constitute on average 37% of the Application operations, forming large and complex dataflow graphs. Reconfigurable functional units (RFUs) are a particularly attractive accelerator for these graphs because they can potentially accelerate many unique graphs with a small amount of additional hardware. In this study, we analyze Application traces of Sandia's scientific Applications and the SPEC-FP benchmark suite. First we select a set of dataflow graphs to accelerate using the RFU, then we use execution-based simulation to determine the Acceleration potential of the Applications when using an RFU. On average, a set of 32 or fewer graphs is sufficient to capture the dataflow behavior of 30% of the integer computation, and more than half of Sandia Applications show an improvement of 5% or more.
Tor M Aamodt - One of the best experts on this subject based on the ideXlab platform.
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a state machine block for high level synthesis
Field-Programmable Technology, 2017Co-Authors: Shadi Assadikhomami, Jennifer Ongko, Tor M AamodtAbstract:FPGAs are being deployed in datacenters to enable improved energy efficiency and Application Acceleration. This paper explores whether FPGA designs can be improved to make them more effective in this new role. We explore the properties of Applications after high-level synthesis has been applied and note that for irregular Applications, a large fraction of FPGA resources may be consumed implementing finite state machines. For many Applications the resulting state machines have states with a single successor and limited fan-out degree. We propose a mixed-grained logic block architecture exploiting these properties that can be integrated into current FPGA architectures, which reduces the area of the next state calculation in FSMs by more than 3x in average without impacting performance.
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FPT - A state machine block for high-level synthesis
2017 International Conference on Field Programmable Technology (ICFPT), 2017Co-Authors: Shadi Assadikhomami, Jennifer Ongko, Tor M AamodtAbstract:FPGAs are being deployed in datacenters to enable improved energy efficiency and Application Acceleration. This paper explores whether FPGA designs can be improved to make them more effective in this new role. We explore the properties of Applications after high-level synthesis has been applied and note that for irregular Applications, a large fraction of FPGA resources may be consumed implementing finite state machines. For many Applications the resulting state machines have states with a single successor and limited fan-out degree. We propose a mixed-grained logic block architecture exploiting these properties that can be integrated into current FPGA architectures, which reduces the area of the next state calculation in FSMs by more than 3x in average without impacting performance.
Matthew Daniel Jacobse - One of the best experts on this subject based on the ideXlab platform.
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smart frame grabber a hardware accelerated computer vision framework
2014Co-Authors: Matthew Daniel JacobseAbstract:Real-time computer vision Applications have difficult runtime constraints within which to execute. Implementing on a CPU provides a baseline for performance. But using custom parallel hardware such as graphics processing units (GPUs) and field programmable gate arrays (FPGAs) represents a cost effective method to achieve greater performance. Greater performance can move an algorithm from non-real-time into the realm of real-time. This opens numerous possibilities for interaction that did not exist before. Tasks such as face detection can be used to set focus points in cameras if performed in real-time. Similarly, body part tracking can be used as input for consumer televisions or video game systems when run in real-time. Acceleration using heterogeneous hardware is attractive because algorithms exhibit different models of computation at different stages of execution. Each platform can be exploited to execute when most efficient. However, it can be difficult to combine these platforms into a single Application. This is due to the lack of reusable components and communication abstractions for these devices. This work describes a framework to lower the barrier for computer vision Application Acceleration called the Smart Frame Grabber Framework. This framework is a collection of reusable hardware Acceleration components that are commonly used for accelerating computer vision Applications using CPUs and FPGAs. It allows Applications to be easily partitioned across multiple heterogenous compute devices. At the heart of this framework is a communication and synchronization platform called RIFFA: A Reusable Integration Framework for FPGA Accelerators. Using the Smart Frame Grabber Framework, researchers can design and build a hardware accelerated computer vision Application in considerably less time and with less upfront effort than it would take using existing vendor provided tools alone
