Abstract:
A method and apparatus for multi-task processing are disclosed. The method includes obtaining a base output corresponding to a first layer, restoring an input map corresponding to a second layer, obtaining an output map corresponding to the second layer, obtaining a delta output map corresponding to the second layer, and storing the base output map and the delta output map.
Abstract:
A memory controller includes a scheduler that decides a processing order of a plurality of requests provided from an external device with reference to a timing parameter value for each of the requests; and a timing control circuit that adjusts the timing parameter value according to a corresponding address to access a memory device, the corresponding address being used to process a corresponding request of the plurality of requests.
Abstract:
A neural network data quantizing method includes: obtaining local quantization data by firstly quantizing, based on a local maximum value for each output channel of a current layer of a neural network, global recovery data obtained by recovering output data of an operation of the current layer based on a global maximum value corresponding to a previous layer of the neural network; storing the local quantization data in a memory to perform an operation of a next layer of the neural network; obtaining global quantization data by secondarily quantizing, based on a global maximum value corresponding to the current layer, local recovery data obtained by recovering the local quantization data based on the local maximum value for each output channel of the current layer; and providing the global quantization data as input data for the operation of the next layer.
Abstract:
A neural network data quantizing method includes: obtaining local quantization data by firstly quantizing, based on a local maximum value for each output channel of a current layer of a neural network, global recovery data obtained by recovering output data of an operation of the current layer based on a global maximum value corresponding to a previous layer of the neural network; storing the local quantization data in a memory to perform an operation of a next layer of the neural network; obtaining global quantization data by secondarily quantizing, based on a global maximum value corresponding to the current layer, local recovery data obtained by recovering the local quantization data based on the local maximum value for each output channel of the current layer; and providing the global quantization data as input data for the operation of the next layer.
Abstract:
A data signal receiver includes a clock signal filter, a falling pulse signal generator, a mixing block, and a sampler. The clock signal filter generates a first filtered clock signal and a second filtered clock signal by filtering a clock signal. The falling pulse signal generator generates a falling pulse signal based on the first filtered clock signal. The mixing block generates a mixed data signal by mixing a data signal and the falling pulse signal. The sampler generates a recovered data signal by sampling the mixed data signal in response to the second filtered clock signal.
Abstract:
A memory controller schedules requests to memory devices according to scores. For this purpose, the memory controller variably adjusts weights for determining the scores with respect to the requests, calculates the scores using the weights, and determines a processing order of the requests according to the scores. The memory controller includes a request queue, a scheduler, and a weight generation circuit. The request queue stores the requests provided from an external device. The scheduler calculates a score for each request included in the request queue and determines the processing order of the requests based on the scores for the requests. The weight generation circuit generates a weight vector including the weights used to calculate the scores.