The Experts below are selected from a list of 8331 Experts worldwide ranked by ideXlab platform
Doug Burger - One of the best experts on this subject based on the ideXlab platform.
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neural acceleration for general purpose approximate programs
Communications of The ACM, 2014Co-Authors: Hadi Esmaeilzadeh, Luis Ceze, Adrian Sampson, Doug BurgerAbstract:As improvements in per-transistor speed and energy efficiency diminish, radical departures from conventional approaches are needed to continue improvements in the performance and energy efficiency of general-purpose processors. One such departure is approximate computing, where error in computation is acceptable and the traditional robust digital abstraction of near-perfect accuracy is relaxed. Conventional techniques in energy-efficient computing navigate a design space defined by the two dimensions of performance and energy, and traditionally trade one for the other. General-purpose approximate computing explores a third dimension---error---and trades the accuracy of computation for gains in both energy and performance. Techniques to harvest large savings from small errors have proven elusive. This paper describes a new approach that uses machine learning-based transformations to accelerate approximation-tolerant programs. The core idea is to train a learning Model how an approximable region of code---code that can produce imprecise but acceptable results---behaves and replace the original code region with an efficient computation of the learned Model. We use neural networks to learn code behavior and approximate it. We describe the Parrot algorithmic transformation, which leverages a simple programmer annotation ("approximable") to transform a code region from a Von Neumann Model to a neural Model. After the learning phase, the compiler replaces the original code with an invocation of a low-power accelerator called a neural processing unit (NPU). The NPU is tightly coupled to the processor pipeline to permit profitable acceleration even when small regions of code are transformed. Offloading approximable code regions to NPUs is faster and more energy efficient than executing the original code. For a set of diverse applications, NPU acceleration provides whole-application speedup of 2.3× and energy savings of 3.0× on average with average quality loss of at most 9.6%. NPUs form a new class of accelerators and show that significant gains in both performance and efficiency are achievable when the traditional abstraction of near-perfect accuracy is relaxed in general-purpose computing.
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general purpose code acceleration with limited precision analog computation
International Symposium on Computer Architecture, 2014Co-Authors: Renee St Amant, Amir Yazdanbakhsh, Jongse Park, Bradley Thwaites, Hadi Esmaeilzadeh, Arjang Hassibi, Luis Ceze, Doug BurgerAbstract:As improvements in per-transistor speed and energy efficiency diminish, radical departures from conventional approaches are becoming critical to improving the performance and energy efficiency of general-purpose processors. We propose a solution--from circuit to compiler-that enables general-purpose use of limited-precision, analog hardwareto accelerate "approximable" code---code that can tolerate imprecise execution. We utilize an algorithmic transformation that automatically converts approximable regions of code from a Von Neumann Model to an "analog" neural Model. We outline the challenges of taking an analog approach, including restricted-range value encoding, limited precision in computation, circuit inaccuracies, noise, and constraints on supported topologies. We address these limitations with a combination of circuit techniques, a hardware/software interface, neuralnetwork training techniques, and compiler support. Analog neural acceleration provides whole application speedup of 3.7x and energy savings of 6.3x with quality loss less than 10% for all except one benchmark. These results show that using limited-precision analog circuits for code acceleration, through a neural approach, is both feasible and beneficial over a range of approximation-tolerant, emerging applications including financial analysis, signal processing, robotics, 3D gaming, compression, and image processing
Hadi Esmaeilzadeh - One of the best experts on this subject based on the ideXlab platform.
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neural acceleration for general purpose approximate programs
Communications of The ACM, 2014Co-Authors: Hadi Esmaeilzadeh, Luis Ceze, Adrian Sampson, Doug BurgerAbstract:As improvements in per-transistor speed and energy efficiency diminish, radical departures from conventional approaches are needed to continue improvements in the performance and energy efficiency of general-purpose processors. One such departure is approximate computing, where error in computation is acceptable and the traditional robust digital abstraction of near-perfect accuracy is relaxed. Conventional techniques in energy-efficient computing navigate a design space defined by the two dimensions of performance and energy, and traditionally trade one for the other. General-purpose approximate computing explores a third dimension---error---and trades the accuracy of computation for gains in both energy and performance. Techniques to harvest large savings from small errors have proven elusive. This paper describes a new approach that uses machine learning-based transformations to accelerate approximation-tolerant programs. The core idea is to train a learning Model how an approximable region of code---code that can produce imprecise but acceptable results---behaves and replace the original code region with an efficient computation of the learned Model. We use neural networks to learn code behavior and approximate it. We describe the Parrot algorithmic transformation, which leverages a simple programmer annotation ("approximable") to transform a code region from a Von Neumann Model to a neural Model. After the learning phase, the compiler replaces the original code with an invocation of a low-power accelerator called a neural processing unit (NPU). The NPU is tightly coupled to the processor pipeline to permit profitable acceleration even when small regions of code are transformed. Offloading approximable code regions to NPUs is faster and more energy efficient than executing the original code. For a set of diverse applications, NPU acceleration provides whole-application speedup of 2.3× and energy savings of 3.0× on average with average quality loss of at most 9.6%. NPUs form a new class of accelerators and show that significant gains in both performance and efficiency are achievable when the traditional abstraction of near-perfect accuracy is relaxed in general-purpose computing.
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general purpose code acceleration with limited precision analog computation
International Symposium on Computer Architecture, 2014Co-Authors: Renee St Amant, Amir Yazdanbakhsh, Jongse Park, Bradley Thwaites, Hadi Esmaeilzadeh, Arjang Hassibi, Luis Ceze, Doug BurgerAbstract:As improvements in per-transistor speed and energy efficiency diminish, radical departures from conventional approaches are becoming critical to improving the performance and energy efficiency of general-purpose processors. We propose a solution--from circuit to compiler-that enables general-purpose use of limited-precision, analog hardwareto accelerate "approximable" code---code that can tolerate imprecise execution. We utilize an algorithmic transformation that automatically converts approximable regions of code from a Von Neumann Model to an "analog" neural Model. We outline the challenges of taking an analog approach, including restricted-range value encoding, limited precision in computation, circuit inaccuracies, noise, and constraints on supported topologies. We address these limitations with a combination of circuit techniques, a hardware/software interface, neuralnetwork training techniques, and compiler support. Analog neural acceleration provides whole application speedup of 3.7x and energy savings of 6.3x with quality loss less than 10% for all except one benchmark. These results show that using limited-precision analog circuits for code acceleration, through a neural approach, is both feasible and beneficial over a range of approximation-tolerant, emerging applications including financial analysis, signal processing, robotics, 3D gaming, compression, and image processing
Luis Ceze - One of the best experts on this subject based on the ideXlab platform.
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neural acceleration for general purpose approximate programs
Communications of The ACM, 2014Co-Authors: Hadi Esmaeilzadeh, Luis Ceze, Adrian Sampson, Doug BurgerAbstract:As improvements in per-transistor speed and energy efficiency diminish, radical departures from conventional approaches are needed to continue improvements in the performance and energy efficiency of general-purpose processors. One such departure is approximate computing, where error in computation is acceptable and the traditional robust digital abstraction of near-perfect accuracy is relaxed. Conventional techniques in energy-efficient computing navigate a design space defined by the two dimensions of performance and energy, and traditionally trade one for the other. General-purpose approximate computing explores a third dimension---error---and trades the accuracy of computation for gains in both energy and performance. Techniques to harvest large savings from small errors have proven elusive. This paper describes a new approach that uses machine learning-based transformations to accelerate approximation-tolerant programs. The core idea is to train a learning Model how an approximable region of code---code that can produce imprecise but acceptable results---behaves and replace the original code region with an efficient computation of the learned Model. We use neural networks to learn code behavior and approximate it. We describe the Parrot algorithmic transformation, which leverages a simple programmer annotation ("approximable") to transform a code region from a Von Neumann Model to a neural Model. After the learning phase, the compiler replaces the original code with an invocation of a low-power accelerator called a neural processing unit (NPU). The NPU is tightly coupled to the processor pipeline to permit profitable acceleration even when small regions of code are transformed. Offloading approximable code regions to NPUs is faster and more energy efficient than executing the original code. For a set of diverse applications, NPU acceleration provides whole-application speedup of 2.3× and energy savings of 3.0× on average with average quality loss of at most 9.6%. NPUs form a new class of accelerators and show that significant gains in both performance and efficiency are achievable when the traditional abstraction of near-perfect accuracy is relaxed in general-purpose computing.
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general purpose code acceleration with limited precision analog computation
International Symposium on Computer Architecture, 2014Co-Authors: Renee St Amant, Amir Yazdanbakhsh, Jongse Park, Bradley Thwaites, Hadi Esmaeilzadeh, Arjang Hassibi, Luis Ceze, Doug BurgerAbstract:As improvements in per-transistor speed and energy efficiency diminish, radical departures from conventional approaches are becoming critical to improving the performance and energy efficiency of general-purpose processors. We propose a solution--from circuit to compiler-that enables general-purpose use of limited-precision, analog hardwareto accelerate "approximable" code---code that can tolerate imprecise execution. We utilize an algorithmic transformation that automatically converts approximable regions of code from a Von Neumann Model to an "analog" neural Model. We outline the challenges of taking an analog approach, including restricted-range value encoding, limited precision in computation, circuit inaccuracies, noise, and constraints on supported topologies. We address these limitations with a combination of circuit techniques, a hardware/software interface, neuralnetwork training techniques, and compiler support. Analog neural acceleration provides whole application speedup of 3.7x and energy savings of 6.3x with quality loss less than 10% for all except one benchmark. These results show that using limited-precision analog circuits for code acceleration, through a neural approach, is both feasible and beneficial over a range of approximation-tolerant, emerging applications including financial analysis, signal processing, robotics, 3D gaming, compression, and image processing
Gernot Heiser - One of the best experts on this subject based on the ideXlab platform.
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the Von Neumann architecture is due for retirement
Workshop on Hot Topics in Operating Systems, 2013Co-Authors: Aleksander Budzynowski, Gernot HeiserAbstract:The processor industry has reached the point where sequential improvements have plateaued and we are being flooded with parallel hardware we don't know how to utilise. An efficient, general-purpose and easy-touse parallel Model is urgently needed to replace the Von Neumann Model. We introduce and discuss the selfmodifying dataflow graph, an unusual Model of computation which combines the naturally parallel dataflow Model with local graph transformations to eliminate the need for a global memory. We justify why it is a promising candidate.
Renee St Amant - One of the best experts on this subject based on the ideXlab platform.
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general purpose code acceleration with limited precision analog computation
International Symposium on Computer Architecture, 2014Co-Authors: Renee St Amant, Amir Yazdanbakhsh, Jongse Park, Bradley Thwaites, Hadi Esmaeilzadeh, Arjang Hassibi, Luis Ceze, Doug BurgerAbstract:As improvements in per-transistor speed and energy efficiency diminish, radical departures from conventional approaches are becoming critical to improving the performance and energy efficiency of general-purpose processors. We propose a solution--from circuit to compiler-that enables general-purpose use of limited-precision, analog hardwareto accelerate "approximable" code---code that can tolerate imprecise execution. We utilize an algorithmic transformation that automatically converts approximable regions of code from a Von Neumann Model to an "analog" neural Model. We outline the challenges of taking an analog approach, including restricted-range value encoding, limited precision in computation, circuit inaccuracies, noise, and constraints on supported topologies. We address these limitations with a combination of circuit techniques, a hardware/software interface, neuralnetwork training techniques, and compiler support. Analog neural acceleration provides whole application speedup of 3.7x and energy savings of 6.3x with quality loss less than 10% for all except one benchmark. These results show that using limited-precision analog circuits for code acceleration, through a neural approach, is both feasible and beneficial over a range of approximation-tolerant, emerging applications including financial analysis, signal processing, robotics, 3D gaming, compression, and image processing
