An electronic device and a control method thereof includes an infrared module which receives an infrared control signal having a predetermined section-specific waveform from an infrared transmitter, a switching unit which turns on/off the infrared module, and a controller which controls the switching unit to alternately turn on and off the infrared module in a predetermined cycle for recognizing the predetermined section-specific waveform of the infrared control signal. Thus, electric power consumed by an infrared module in a standby mode may be reduced while receiving a signal of an infrared transmitter.

Electronic device and control method thereof

Methods, apparatuses, and systems related to use of error correction pointers (ECPs) to handle hard errors in memory are described herein. In embodiments, a read module of a memory controller may read a codeword stored in a memory. The read module may determine a number of hard errors in the codeword. Responsive to a determination that the number of hard errors exceeds a threshold, the read module may store ECP information associated with the hard errors. The read module may include an error correction code (ECC) module to perform an ECC process on the codeword. The read module may use the ECP information to decode the codeword to recover the data responsive to a determination that the ECC process failed. Other embodiments may be described and claimed.

Use of error correction pointers to handle errors in memory

An apparatus and method are described for distributed and cooperative computation in artificial neural networks. For example, one embodiment of an apparatus comprises: an input/output (I/O) interface; a plurality of processing units communicatively coupled to the I/O interface to receive data for input neurons and synaptic weights associated with each of the input neurons, each of the plurality of processing units to process at least a portion of the data for the input neurons and synaptic weights to generate partial results; and an interconnect communicatively coupling the plurality of processing units, each of the processing units to share the partial results with one or more other processing units over the interconnect, the other processing units using the partial results to generate additional partial results or final results. The processing units may share data including input neurons and weights over the shared input bus.

Method and apparatus for distributed and cooperative computation in artificial neural networks

A method and system for domain adaptation based on multi-layer fusion in a convolutional neural network architecture for feature extraction and a two-step training and fine-tuning scheme. The architecture concatenates features extracted at different depths of the network to form a fully connected layer before the classification step. First, the network is trained with a large set of images from a source domain as a feature extractor. Second, for each new domain (including the source domain), the classification step is fine-tuned with images collected from the corresponding site. The features from different depths are concatenated with and fine-tuned with weights adjusted for a specific task. The architecture is used for classifying high occupancy vehicle images.

Multi-layer fusion in a convolutional neural network for image classification

A method for implementing a convolutional neural network (CNN) accelerator on a target includes utilizing one or more processing elements to implement a standard convolution layer. A configuration of the CNN accelerator is modified to change a data flow between components on the CNN accelerator. The one or more processing elements is utilized to implement a fully connected layer in response to the change in the data flow.

Method and Apparatus for Implementing Layers on a Convolutional Neural Network Accelerator

A processing device includes a processor core and a number of calculation modules that each is configurable to perform any one of operations for a convolutional neuron network system. A first set of the calculation modules are configured to perform convolution operations, a second set of the calculation modules are reconfigured to perform averaging operations, and a third set of the calculation modules are reconfigured to perform dot product operations.

Reconfigurable processing unit

Systems and methods for performing convolution operations. An example processing system comprises: a processing core; and a convolver unit to apply a convolution filter to a plurality of input data elements represented by a two-dimensional array, the convolver unit comprising a plurality of multipliers coupled to two or more sets of latches, wherein each set of latches is to store a plurality of data elements of a respective one-dimensional section of the two-dimensional array.

Processing device for performing convolution operations

Embodiments of apparatuses, methods, and storage medium associated with selectively providing error correction to memory are disclosed herein. In one instance, an apparatus may include a memory controller configured to control access to a non-volatile memory having storage locations. The controller may be configured to provide a first error correction arrangement to provide a first level of error correction capability for data stored in the non-volatile memory. The memory controller may include a control/error correction block configured to provide a second error correction arrangement with a second level of error correction capability for data stored in the non-volatile memory. The second level of error correction capability enables correction of at least one bit error more than the first level. The memory controller may be configured to selectively employ the second error correction arrangement to complement the first error correction arrangement. Other embodiments may be described and claimed.

Selective provision of error correction for memory

A method of determining vulnerability of a cache memory includes associating a first counter with a cache element and periodically incrementing the first counter. When a read or other access that consumes the data in the cache element occurs, a current value of the first counter is accumulated. When a write or other cache access that modifies data in the cache element occurs, the first counter is reset. At the end of an evaluation period, the value in a total counter approximates the number of clock cycles during which data that was consumed was vulnerable. Dividing this value by the number of clock cycles approximates the vulnerability of this cache element. The vulnerability for a subset of all cache elements may be measured and extrapolated to obtain an estimate for the vulnerability of the cache memory as a whole.

Enric Herrero Abellanas

Papers

Method and apparatus for distributed and cooperative computation in artificial neural networks

Reconfigurable processing unit

Processing device for performing convolution operations

Selective provision of error correction for memory

Vulnerability estimation for cache memory