Abstract: Embodiments of an electronic device include an integrated circuit, a reconfigurable stream switch formed in the integrated circuit along with a plurality of convolution accelerators and a decompression unit coupled to the reconfigurable stream switch. The decompression unit decompresses encoded kernel data in real time during operation of convolutional neural network.

Abstract: Embodiments of a device include an integrated circuit, a reconfigurable stream switch formed in the integrated circuit along with a plurality of convolution accelerators and an arithmetic unit coupled to the reconfigurable stream switch. The arithmetic unit has at least one input and at least one output. The at least one input is arranged to receive streaming data passed through the reconfigurable stream switch, and the at least one output is arranged to stream resultant data through the reconfigurable stream switch. The arithmetic unit also has a plurality of data paths. At least one of the plurality of data paths is solely dedicated to performance of operations that accelerate an activation function represented in the form of a piece-wise second order polynomial approximation.

Abstract: A device include on-board memory, an applications processor, a digital signal processor (DSP) cluster, a configurable accelerator framework (CAF), and a communication bus architecture. The communication bus communicatively couples the applications processor, the DSP cluster, and the CAF to the on-board memory. The CAF includes a reconfigurable stream switch and data volume sculpting circuitry, which has an input and an output coupled to the reconfigurable stream switch. The data volume sculpting circuitry receives a series of frames, each frame formed as a two dimensional (2D) data structure, and determines a first dimension and a second dimension of each frame of the series of frames. Based on the first and second dimensions, the data volume sculpting circuitry determines for each frame a position and a size of a region-of-interest to be extracted from the respective frame, and extracts from each frame, data in the frame that is within the region-of-interest.

Abstract: An integrated circuit includes a reconfigurable stream switch and an arithmetic circuit. The stream switch, in operation, streams data. The arithmetic circuit has a plurality of inputs coupled to the reconfigurable stream switch. In operation, the arithmetic circuit generates an output according to AX+BY+C, where A, B and C are vector or scalar constants, and X and Y are data streams streamed to the arithmetic circuit through the reconfigurable stream switch.

Abstract: A convolutional neural network includes a pooling unit. The pooling unit performs pooling operations between convolution layers of the convolutional neural network. The pooling unit includes hardware blocks that promote computational and area efficiency in the convolutional neural network.

Abstract: The present invention relates to processes for extracting lipid from vegetative plant parts such as leaves, stems, roots and tubers, and for producing industrial products such as hydrocarbon products from the lipids. Preferred industrial products include alkyl esters which may be blended with petroleum based fuels.

Abstract: The present invention relates to microbial cells comprising triacylglycerol (TAG) with short chain fatty acids (SCFA), as well as methods of using these cells to produce lipid comprising TAG with SCFAs.

Abstract: The present invention relates to extracted plant lipid or microbial lipid comprising docosapentaenoic acid, and processes for producing the extracted lipid.

Abstract: A device include on-board memory, an applications processor, a digital signal processor (DSP) cluster, a configurable accelerator framework (CAF), and at least one communication bus architecture. The communication bus communicatively couples the applications processor, the DSP cluster, and the CAF to the on-board memory. The CAF includes a reconfigurable stream switch and data volume sculpting circuitry, which has an input and an output coupled to the reconfigurable stream switch. The data volume sculpting circuitry receives a series of frames, each frame formed as a two dimensional (2D) data structure, and determines a first dimension and a second dimension of each frame of the series of frames. Based on the first and second dimensions, the data volume sculpting circuitry determines for each frame a position and a size of a region-of-interest to be extracted from the respective frame, and extracts from each frame, data in the frame that is within the region-of-interest.

Abstract: Techniques and systems are provided for implementing a convolutional neural network. One or more convolution accelerators are provided that each include a feature line buffer memory, a kernel buffer memory, and a plurality of multiply-accumulate (MAC) circuits arranged to multiply and accumulate data. In a first operational mode the convolutional accelerator stores feature data in the feature line buffer memory and stores kernel data in the kernel data buffer memory. In a second mode of operation, the convolutional accelerator stores kernel decompression tables in the feature line buffer memory.

Abstract: The present invention relates to methods of synthesizing long-chain polyunsaturated fatty acids, especially eicosapentaenoic acid, docosapentaenoic acid and docosahexaenoic acid, in recombinant cells such as yeast or plant cells. Also provided are recombinant cells or plants which produce long-chain polyunsaturated fatty acids. Furthermore, the present invention relates to a group of new enzymes which possess desaturase or elongase activity that can be used in methods of synthesizing long-chain polyunsaturated fatty acids.

Abstract: A convolutional neural network includes convolution circuitry. The convolution circuitry performs convolution operations on input tensor values. The convolutional neural network includes requantization circuitry that requantizes convolution values output from the convolution circuitry.

Abstract: Embodiments of an electronic device include an integrated circuit, a reconfigurable stream switch formed in the integrated circuit along with a plurality of convolution accelerators and a decompression unit coupled to the reconfigurable stream switch. The decompression unit decompresses encoded kernel data in real time during operation of convolutional neural network.

Abstract: The present invention relates to processes for producing industrial products such as hydrocarbon products from non-polar lipids in a vegetative plant part. Preferred industrial products include alkyl esters which may be blended with petroleum based fuels.

Abstract: Embodiments of a device include an integrated circuit, a reconfigurable stream switch formed in the integrated circuit, and an arithmetic unit coupled to the reconfigurable stream switch. The arithmetic unit has a plurality of inputs and at least one output, and the arithmetic unit is solely dedicated to performance of a plurality of parallel operations. Each one of the plurality of parallel operations carries out a portion of the formula: output=AX+BY+C.

Abstract: A system for data reconciliation is provided. The data reconciliation system includes a data processing subsystem. The data processing subsystem includes a computation module, configured to generate hash values for a set of tables located in a source database by a hashing technique and also configured generate hash values for a set of tables located in a destination database by the hashing technique. The data processing subsystem also includes an analysis module, configured to analyse the hash values located in the source database and the hash values located in the destination database by a pre-determined rule. The data processing subsystem also includes a suggestion module, configured to suggest output based on the analysis result. A data memory subsystem is configured to store the generated hash values for the source database and the generated hash values for the destination database. Present invention provides safe migration or transferring of data.

Abstract: The present invention relates to genetically modified cells that are capable of optimal transgene expression by co-expressing a silencing suppressor whilst at the same time are also capable of silencing a gene, such as a naturally occurring gene of the cell. The present invention also relates to methods of producing the modified cells, as well as relates to processes for obtaining a genetically modified cell with a desired property.

Abstract: Data structure and microcontroller architecture performing binary multiply-accumulate operations using multiple partial copies of weights. Destination-register location, source-register location, and weight-register location are received. Using the weight-register location, a sub-set of the weight bits is copied a select number of times based on a filter index value that is received. Each copy of the sub-set of weights is executed in parallel. Using the source-register location, a sub-set of the input bits is selected based on the size of the sub-set of weights, wherein the sub-set of input bits is shifted one bit from a previous sub-set of input bits. XOR operation is performed on each corresponding bit in the copy of the sub-set of weights with each corresponding bit in the selected sub-set of input bits. In a corresponding destination sub-location, output of each XOR operation is aggregated with each other and with current value of the corresponding destination sub-location.

Abstract: Techniques and systems are provided for implementing a convolutional neural network. One or more convolution accelerators are provided that each include a feature line buffer memory, a kernel buffer memory, and a plurality of multiply-accumulate (MAC) circuits arranged to multiply and accumulate data. In a first operational mode the convolutional accelerator stores feature data in the feature line buffer memory and stores kernel data in the kernel data buffer memory. In a second mode of operation, the convolutional accelerator stores kernel decompression tables in the feature line buffer memory.

Abstract: A convolutional neural network includes a pooling unit. The pooling unit performs pooling operations between convolution layers of the convolutional neural network. The pooling unit includes hardware blocks that promote computational and area efficiency in the convolutional neural network.