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公开(公告)号：US20190205358A1
公开(公告)日：2019-07-04
申请号：US15857918
申请日：2017-12-29
申请人： Facebook, Inc.
CPC分类号： G06F17/16 , G06F7/5443 , G06N3/0481 , G06N3/063
摘要： A special-purpose, hardware-based accelerator may include an input subsystem configured to receive first and second vectors as operands of a full dot-product operation. The accelerator may also include a sparsity-aware dot-product engine communicatively coupled to the input subsystem and configured to perform adaptive dot-product processing by: (1) identifying, within the first and second vectors, at least one zero-value element and (2) executing, in response to identifying the zero-value element, a reduced dot-product operation that excludes, relative to the full dot-product operation, at least one mathematical operation in which the zero-value element is an operand. The accelerator may also include an output subsystem that is communicatively coupled to the sparsity-aware dot-product engine and configured to send a result of the reduced dot-product operation to a storage subsystem. Various other accelerators, computing systems, and methods are also disclosed.
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公开(公告)号：US10817783B1
公开(公告)日：2020-10-27
申请号：US16868936
申请日：2020-05-07
申请人： Facebook, Inc.
摘要： The disclosed computer-implemented method for efficiently updating neural networks may include (i) identifying a neural network that comprises sets of interconnected nodes represented at least in part by a plurality of matrices and that is trained on a training computing device and executes on at least one endpoint device, (ii) constraining a training session for the neural network to reduce the size in memory of the difference between the previous values of the matrices prior to the training session and the new values of the matrices after the training session, (iii) creating a delta update for the neural network that describes the difference between the previous values and the new values, and (iv) updating the neural network on the endpoint device to the new state by sending the delta update from the training computing device to the endpoint computing device. Various other methods, systems, and computer-readable media are also disclosed.
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公开(公告)号：US20200160848A1
公开(公告)日：2020-05-21
申请号：US16749328
申请日：2020-01-22
申请人： Facebook, Inc.
摘要： The disclosed method may include (1) determining whether a next operation of a plurality of operations of an artificial neural network (ANN) is dependent upon a Boolean predication value based on a representative value for a weight or an input of a node of the ANN, (2) based on the next operation not being dependent on the Boolean predication value, allowing the next operation to update a state of the ANN, and (3) based on the next operation being dependent on the Boolean predication value, performing at least one of (a) allowing, based on the Boolean predication value being a first value, the next operation to update the state of the ANN, and (b) preventing, based on the Boolean predication value being a second value different from the first value, the next operation from updating the state of the ANN. Various other methods and systems are also disclosed.
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公开(公告)号：US10474430B2
公开(公告)日：2019-11-12
申请号：US15857998
申请日：2017-12-29
申请人： Facebook, Inc.
摘要： The disclosed method may include (1) receiving a precision level of each weight associated with each input of a node of a computational model, (2) identifying, for each weight, one of a plurality of multiplier groups, where each multiplier group may include a plurality of hardware multipliers of a corresponding bit width, and where the corresponding bit width of the plurality of hardware multipliers of the one of the plurality of multiplier groups may be sufficient to multiply the weight by the associated input, and (3) multiplying each weight by its associated input using an available hardware multiplier of the one of the plurality of multiplier groups identified for the weight. Various other processing elements, methods, and systems are also disclosed.
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公开(公告)号：US20190206390A1
公开(公告)日：2019-07-04
申请号：US15857990
申请日：2017-12-29
申请人： Facebook, Inc.
摘要： The disclosed method may include (1) determining whether a next operation of a plurality of operations of a computational model is dependent upon a Boolean predication value, (2) based on the next operation not being dependent on the Boolean predication value, performing the next operation, where a state of the computational model is updated as a result of performing the next operation, and (3) based on the next operation being dependent on the Boolean predication value, performing at least one of (a) allowing, based on the Boolean predication value being a first value, the next operation to update the state of the computational model, and (b) preventing, based on the Boolean predication value being a second value different from the first value, the next operation from updating the state of the computational model. Various other methods and systems are also disclosed.
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公开(公告)号：US11023240B1
公开(公告)日：2021-06-01
申请号：US16692899
申请日：2019-11-22
申请人： Facebook, Inc.
发明人： Nadav Rotem , Jong Soo Park , Zhaoxia Deng , Abdulkadir Utku Diril , Mikhail Smelyanskiy , Roman Dzhabarov , James Hegeman
摘要： The disclosed computer-implemented method may include receiving an input value and a floating-point scaling factor and determining (1) an integer scaling factor based on the floating-point scaling factor, (2) a pre-scaling adjustment value representative of a number of places by which to shift a binary representation of the input value prior to a scaling operation, and (3) a post-scaling adjustment value representative of a number of places by which to shift the binary representation of the input value following the scaling operation. The method may further include calculating a scaled result value by (1) shifting rightwards the binary representation of the input value by the pre-scaling adjustment value, (2) scaling the shifted binary representation of the input value by the integer scaling factor, and (3) shifting rightwards the shifted and scaled binary value by the post-scaling adjustment value. Various other methods, systems, and computer-readable media are also disclosed.
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公开(公告)号：US10699190B1
公开(公告)日：2020-06-30
申请号：US15911120
申请日：2018-03-04
申请人： Facebook, Inc.
摘要： The disclosed computer-implemented method for efficiently updating neural networks may include (i) identifying a neural network that comprises sets of interconnected nodes represented at least in part by a plurality of matrices and that is trained on a training computing device and executes on at least one endpoint device, (ii) constraining a training session for the neural network to reduce the size in memory of the difference between the previous values of the matrices prior to the training session and the new values of the matrices after the training session, (iii) creating a delta update for the neural network that describes the difference between the previous values and the new values, and (iv) updating the neural network on the endpoint device to the new state by sending the delta update from the training computing device to the endpoint computing device. Various other methods, systems, and computer-readable media are also disclosed.
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公开(公告)号：US10579383B1
公开(公告)日：2020-03-03
申请号：US15992793
申请日：2018-05-30
申请人： Facebook, Inc.
发明人： Nadav Rotem , Jong Soo Park , Zhaoxia Deng , Abdulkadir Utku Diril , Mikhail Smelyanskiy , Roman Dzhabarov , James Wesley Hegeman
摘要： The disclosed computer-implemented method may include receiving an input value and a floating-point scaling factor and determining (1) an integer scaling factor based on the floating-point scaling factor, (2) a pre-scaling adjustment value representative of a number of places by which to shift a binary representation of the input value prior to a scaling operation, and (3) a post-scaling adjustment value representative of a number of places by which to shift the binary representation of the input value following the scaling operation. The method may further include calculating a scaled result value by (1) shifting rightwards the binary representation of the input value by the pre-scaling adjustment value, (2) scaling the shifted binary representation of the input value by the integer scaling factor, and (3) shifting rightwards the shifted and scaled binary value by the post-scaling adjustment value. Various other methods, systems, and computer-readable media are also disclosed.
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公开(公告)号：US10553207B2
公开(公告)日：2020-02-04
申请号：US15857990
申请日：2017-12-29
申请人： Facebook, Inc.
摘要： The disclosed method may include (1) determining whether a next operation of a plurality of operations of a computational model is dependent upon a Boolean predication value, (2) based on the next operation not being dependent on the Boolean predication value, performing the next operation, where a state of the computational model is updated as a result of performing the next operation, and (3) based on the next operation being dependent on the Boolean predication value, performing at least one of (a) allowing, based on the Boolean predication value being a first value, the next operation to update the state of the computational model, and (b) preventing, based on the Boolean predication value being a second value different from the first value, the next operation from updating the state of the computational model. Various other methods and systems are also disclosed.
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公开(公告)号：US10482156B2
公开(公告)日：2019-11-19
申请号：US15857918
申请日：2017-12-29
申请人： Facebook, Inc.
摘要： A special-purpose, hardware-based accelerator may include an input subsystem configured to receive first and second vectors as operands of a full dot-product operation. The accelerator may also include a sparsity-aware dot-product engine communicatively coupled to the input subsystem and configured to perform adaptive dot-product processing by: (1) identifying, within the first and second vectors, at least one zero-value element and (2) executing, in response to identifying the zero-value element, a reduced dot-product operation that excludes, relative to the full dot-product operation, at least one mathematical operation in which the zero-value element is an operand. The accelerator may also include an output subsystem that is communicatively coupled to the sparsity-aware dot-product engine and configured to send a result of the reduced dot-product operation to a storage subsystem. Various other accelerators, computing systems, and methods are also disclosed.
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