| Patent application number | Description | Published |
|---|
| 20080320130 | VISIBILITY AND CONTROL OF WIRELESS SENSOR NETWORKS - A computer implemented technique framework, prototype tool and associated methods that provide a high degree of visibility and control over the in-field execution of software in a minimally intrusive manner wherein developer-defined correctness tests and validation logic are embedded into the sensor node itself, making in-field software testing autonomous without necessitating continuous developer participation. | 12-25-2008 |
| 20100088492 | SYSTEMS AND METHODS FOR IMPLEMENTING BEST-EFFORT PARALLEL COMPUTING FRAMEWORKS - Implementations of the present principles include Best-effort computing systems and methods. In accordance with various exemplary aspects of the present principles, a application computation requests directed to a processing platform may be intercepted and classified as either guaranteed computations or best-effort computations. Best-effort computations may be dropped to improve processing performance while minimally affecting the end result of application computations. In addition, interdependencies between best-effort computations may be relaxed to improve parallelism and processing speed while maintaining accuracy of computation results. | 04-08-2010 |
| 20110029471 | DYNAMICALLY CONFIGURABLE, MULTI-PORTED CO-PROCESSOR FOR CONVOLUTIONAL NEURAL NETWORKS - A coprocessor and method for processing convolutional neural networks includes a configurable input switch coupled to an input. A plurality of convolver elements are enabled in accordance with the input switch. An output switch is configured to receive outputs from the set of convolver elements to provide data to output branches. A controller is configured to provide control signals to the input switch and the output switch such that the set of convolver elements are rendered active and a number of output branches are selected for a given cycle in accordance with the control signals. | 02-03-2011 |
| 20110119467 | MASSIVELY PARALLEL, SMART MEMORY BASED ACCELERATOR - Systems and methods for massively parallel processing on an accelerator that includes a plurality of processing cores. Each processing core includes multiple processing chains configured to perform parallel computations, each of which includes a plurality of interconnected processing elements. The cores further include multiple of smart memory blocks configured to store and process data, each memory block accepting the output of one of the plurality of processing chains. The cores communicate with at least one off-chip memory bank. | 05-19-2011 |
| Patent application number | Description | Published |
|---|
| 20090119556 | METHOD AND APPARATUS FOR TESTING LOGIC CIRCUIT DESIGNS - Disclosed is a logic testing system that includes a decompressor and a tester in communication with the decompressor. The tester is configured to store a seed and locations of scan inputs and is further configured to transmit the seed and the locations of scan inputs to the decompressor. The decompressor is configured to generate a test pattern from the seed and the locations of scan inputs. The decompressor includes a first test pattern generator, a second test pattern generator, and a selector configured to select the test pattern generated by the first test pattern generator or the test pattern generated by the second test pattern generator using the locations of scan inputs. | 05-07-2009 |
| 20090119563 | METHOD AND APPARATUS FOR TESTING LOGIC CIRCUIT DESIGNS - Disclosed is a logic testing system that includes a decompressor and a tester in communication with the decompressor. The tester is configured to store a seed and locations of scan inputs and is further configured to transmit the seed and the locations of scan inputs to the decompressor. The decompressor is configured to generate a test pattern from the seed and the locations of scan inputs. The decompressor includes a first test pattern generator, a second test pattern generator, and a selector configured to select the test pattern generated by the first test pattern generator or the test pattern generated by the second test pattern generator using the locations of scan inputs. | 05-07-2009 |
| 20090210762 | Method for Blocking Unknown Values in Output Response of Scan Test Patterns for Testing Circuits - A method includes compressing control patterns describing values required at the control signals of blocking logic gates, by linear feedback shift register LFSR reseeding; bypassing blocking logic gates for some groups of scan chains that do not capture unknown values in output response of scan test patterns for testing circuits; and reducing numbers of specified bits in densely specified ones of the control patterns for further reducing the size of a seed of the LFSR. | 08-20-2009 |
| 20090304268 | System and Method for Parallelizing and Accelerating Learning Machine Training and Classification Using a Massively Parallel Accelerator - A method system for training an apparatus to recognize a pattern includes providing the apparatus with a host processor executing steps of a machine learning process; providing the apparatus with an accelerator including at least two processors; inputting training pattern data into the host processor; determining coefficient changes in the machine learning process with the host processor using the training pattern data; transferring the training data to the accelerator; determining kernel dot-products with the at least two processors of the accelerator using the training data; and transferring the dot-products back to the host processor. | 12-10-2009 |
| 20100088490 | METHODS AND SYSTEMS FOR MANAGING COMPUTATIONS ON A HYBRID COMPUTING PLATFORM INCLUDING A PARALLEL ACCELERATOR - In accordance with exemplary implementations, application computation operations and communications between operations on a host processing platform may be adapted to conform to the memory capacity of a parallel accelerator. Computation operations may be split and scheduled such that the computation operations fit within the memory capacity of the accelerator. Further, the operations may be automatically adapted without any modification to the code of an application. In addition, data transfers between a host processing platform and the parallel accelerator may be minimized in accordance with exemplary aspects of the present principles to improve processing performance. | 04-08-2010 |
