Abstract: Apparatus and methods are disclosed for performing block floating-point (BFP) operations, including in implementations of neural networks. All or a portion of one or more matrices or vectors can share one or more common exponents. Techniques are disclosed for selecting the shared common exponents. In some examples of the disclosed technology, a method includes producing BFP representations of matrices or vectors, at least two elements of the respective matrices or vectors sharing a common exponent, performing a mathematical operation on two or more of the plurality of matrices or vectors, and producing an output matrix or vector. Based on the output matrix or vector, one or more updated common exponents are selected, and an updated matrix or vector is produced having some elements that share the updated common exponents.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: Apparatus and methods are disclosed for performing block floating-point (BFP) operations, including in implementations of neural networks. All or a portion of one or more matrices or vectors can share one or more common exponents. Techniques are disclosed for selecting the shared common exponents. In some examples of the disclosed technology, a method includes producing BFP representations of matrices or vectors, at least two elements of the respective matrices or vectors sharing a common exponent, performing a mathematical operation on two or more of the plurality of matrices or vectors, and producing an output matrix or vector. Based on the output matrix or vector, one or more updated common exponents are selected, and an updated matrix or vector is produced having some elements that share the updated common exponents.

Abstract: A system may include a Graphics Processing Unit (GPU) and a Field Programmable Gate Array (FPGA). The system may further include a bus interface that is external to the FPGA, and that is configured to transfer data directly between the GPU and the FPGA without storing the data in a memory of a central processing unit (CPU) as an intermediary operation.

Abstract: Apparatus and methods are disclosed for performing block floating-point (BFP) operations, including in implementations of neural networks. All or a portion of one or more matrices or vectors can share one or more common exponents. Techniques are disclosed for selecting the shared common exponents. In some examples of the disclosed technology, a method includes producing BFP representations of matrices or vectors, at least two elements of the respective matrices or vectors sharing a common exponent, performing a mathematical operation on two or more of the plurality of matrices or vectors, and producing an output matrix or vector. Based on the output matrix or vector, one or more updated common exponents are selected, and an updated matrix or vector is produced having some elements that share the updated common exponents.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: A system may include a Graphics Processing Unit (GPU) and a Field Programmable Gate Array (FPGA). The system may further include a bus interface that is external to the FPGA, and that is configured to transfer data directly between the GPU and the FPGA without storing the data in a memory of a central processing unit (CPU) as an intermediary operation.

Abstract: Apparatus and methods are disclosed for performing block floating-point (BFP) operations, including in implementations of neural networks. All or a portion of one or more matrices or vectors can share one or more common exponents. Techniques are disclosed for selecting the shared common exponents. In some examples of the disclosed technology, a method includes producing BFP representations of matrices or vectors, at least two elements of the respective matrices or vectors sharing a common exponent, performing a mathematical operation on two or more of the plurality of matrices or vectors, and producing an output matrix or vector. Based on the output matrix or vector, one or more updated common exponents are selected, and an updated matrix or vector is produced having some elements that share the updated common exponents.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: A system may include a Graphics Processing Unit (GPU) and a Field Programmable Gate Array (FPGA). The system may further include a bus interface that is external to the FPGA, and that is configured to transfer data directly between the GPU and the FPGA without storing the data in a memory of a central processing unit (CPU) as an intermediary operation.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: A system may include a Graphics Processing Unit (GPU) and a Field Programmable Gate Array (FPGA). The system may further include a bus interface that is external to the FPGA, and that is configured to transfer data directly between the GPU and the FPGA without storing the data in a memory of a central processing unit (CPU) as an intermediary operation.

Abstract: A system may include a Graphics Processing Unit (GPU) and a Field Programmable Gate Array (FPGA). The system may further include a bus interface that is external to the FPGA, and that is configured to transfer data directly between the GPU and the FPGA without storing the data in a memory of a central processing unit (CPU) as an intermediary operation.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: Embodiments are described that leverage variability of a chip. Different areas of a chip vary in terms of reliability under a same operating condition. The variability may be captured by measuring errors over different areas of the chip. A physical factor that affects or controls the likelihood of an error on the chip can be varied. For example, the voltage supplied to a chip may be provided at different levels. At each level of the physical factor, the chip is tested for errors within the regions. Some indication of the error statistics for the regions is stored and then used to adjust power used by the chip, to adjust reliability behavior of the chip, to allow applications to control how the chip is used, to compute a signature uniquely identifying the chip, etc.

Abstract: A set of population data that includes a plurality of individual population data entities is obtained. Each of the individual population data entities in the obtained set is streamed to an array of a plurality of evaluation functions. The evaluation functions are configured to evaluate each entity to determine an acceptability of the entity for a current state of a candidate centroid value associated with the evaluation function. Acceptance of input data entities is terminated after a first accepting one of the evaluation functions accepts an entity, based on the determined acceptability and on a predetermined priority ordering of acceptance. The first accepting one of the evaluation functions, in the priority ordering, incorporates population data associated with the accepted entity into an aggregator that is local to the first accepting evaluation function.

Abstract: A system may include a Graphics Processing Unit (GPU) and a Field Programmable Gate Array (FPGA). The system may further include a bus interface that is external to the FPGA, and that is configured to transfer data directly between the GPU and the FPGA without storing the data in a memory of a central processing unit (CPU) as an intermediary operation.