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 memory controller includes a request queue that stores requests provided from an external device, a scheduler that calculates a score for each request included in the request queue and determines a processing order of the requests based on the scores for the requests, and a weight generation circuit that generates a weight vector including weights used to calculated the scores.
Abstract:
An encoding method includes receiving input data represented by a 16-bit half floating point, adjusting a number of bits of an exponent and a mantissa of the input data to split the input data into 4-bit units, and encoding the input data in which the number of bits has been adjusted such that the exponent is a multiple of “4”.
Abstract:
A processor-implemented method with multi-task processing includes: obtaining weights of a first neural network; obtaining first delta weights of a second neural network that is fine-tuned from the first neural network, based on a target task; performing an operation of the second neural network on first input data, based on sums of the weights of the first neural network and the first delta weights; obtaining second delta weights of a third neural network that is fine-tuned from the first neural network, based on a change of the target task; replacing the first delta weights with the second delta weights; and performing an operation of the third neural network on second input data, based on sums of the weights of the first neural network and the second delta weights, wherein the first delta weights comprise difference values in the weights of the first neural network and weights of the second neural network, and the second delta weight comprises difference values in the weights of the first neural network and weights of the third neural network.
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 method with neural network compression includes: generating a second neural network by fine-tuning a first neural network, which is pre-trained based on training data, for a predetermined purpose; determining delta weights corresponding to differences between weights of the first neural network and weights of the second neural network; compressing the delta weights; retraining the second neural network updated based on the compressed delta weights and the weights of the first neural network; and encoding and storing the delta weights updated by the retraining of the second neural network.