An apparatus includes a first encoder circuit configured to compress a block of data using dictionary based compression and a second encoder circuit connected to the first encoder circuit to receive the compressed block of data from the first encoder circuit. The second encoder circuit is configured to further compress the compressed block of data according to a codebook. The codebook is based on a distribution of data of a prior block of data or a distribution of data of a portion of the block of data that is less than the block of data. The operation of the second encoder circuit overlaps with the operation of the first encoder circuit to achieve high throughput and avoid the need for a large block of memory (e.g., SRAM) to occupy the data in flight until the second encoder circuit can start.

Devices included in a wireless neural interface system are disclosed. According to an embodiment, an internal device of a wireless neural interface system that performs communication through a human body channel may include: bio-electrodes including a first electrode, a second electrode, a third electrode, and a fourth electrode that are attached inside a human body; an oversampling converter configured to detect a biosignal on the basis of the first electrode and the second electrode, and output an oversampling signal by oversampling the biosignal; a wireless signal transmitter configured to encode the oversampling signal on the basis of a human body channel characteristic, and transmit the encoded oversampling signal to an external device, which is connected with the internal device through the human channel, through the third electrode; and a wireless power receiver configured to supply power to the oversampling converter and the wireless signal transmitter in response to a power signal received from the external device through the fourth electrode.

Systems and methods for genome sequence compression and decompression are provided. The method for compression encoding of a genome sequence includes partitioning a genome sequence into a plurality of Group of Bases (GoBs) and processing each of the plurality of GoBs independently to encode the genome sequence into a bit stream. Processing each of the plurality of GoBs includes dividing each of the plurality of GOBs into a first part and a second part, the first part including an initial context part and the second part including a learning-based inference part. The processing each of the plurality of GoBs further includes encoding the first part in accordance with a Markov model, encoding the second part in accordance with a learning-based model, and encoding the encoded first part and the encoded second part into the bit stream with an arithmetic encoder. The learning-based model may include Long and Short-Term Memory (LSTM)-based neural networks.

A compute device to manage workflow to disaggregated computing resources is provided. The compute device comprises a compute engine receive a workload processing request, the workload processing request defined by at least one request parameter, determine at least one accelerator device capable of processing a workload in accordance with the at least one request parameter, transmit a workload to the at least one accelerator device, receive a work product produced by the at least one accelerator device from the workload, and provide the work product to an application.

The present disclosure relates to methods and apparatus for audio coding. A method of encoding a portion of an audio signal comprises determining whether the portion of the audio signal is likely to contain dense transient events, and if it is determined that the portion of the audio signal is likely to contain dense transient events, quantizing the portion of the audio signal using a quantization 5 mode that applies a substantially constant signal-to-noise ratio over frequency for the portion of the audio signal. The present disclosure further relates to a method of detecting dense transient events in a portion of an audio signal.

A two-dimensional square constraint encoding and decoding method and device, relating to the fields of data storage and data communication. The encoding method comprises: caching a one-dimensional data stream, and dividing the one-dimensional data stream into several sets of one-dimensional 2-bit data; according to an encoding table, encoding each set of 2-bit data into a 3*2 two-dimensional codeword in sequence by an encoder, and then cascading all the two-dimensional codewords into a two-dimensional constraint array in a specified order; the decoding method comprises: reading the two-dimensional constraint array by a decoder, and dividing the two-dimensional constraint array into several 3*2 two-dimensional codewords; decoding each two-dimensional codeword into the one-dimensional 2-bit data in sequence through a decoding table, and then successively assembling the generated one-dimensional 2-bit data into the one-dimensional data stream and outputting. The two-dimensional square constraint in the present invention means that in a binary data array composed of data “0” and “1”, along four directions of a horizontal direction, a vertical direction, a northeast direction and a southeast direction, the two data “1” cannot be directly adjacent to each other.

A two-dimensional square constraint encoding and decoding method and device, relating to the fields of data storage and data communication. The encoding method includes caching a one-dimensional data stream, and dividing the one-dimensional data stream into several sets of one-dimensional 2-bit data; according to an encoding table, encoding each set of 2-bit data into a 3*2 two-dimensional codeword in sequence by an encoder, and then cascading all the two-dimensional codewords into a two-dimensional constraint array in a specified order; the decoding method includes reading the two-dimensional constraint array by a decoder, and dividing the two-dimensional constraint array into several 3*2 two-dimensional codewords; decoding each two-dimensional codeword into the one-dimensional 2-bit data in sequence through a decoding table, and then successively assembling the generated one-dimensional 2-bit data into the one-dimensional data stream and outputting.

Techniques are disclosed relating to detecting matching datasets using encode values. In various embodiments, a data monitoring system may perform encoding operations on a first dataset to generate a first encode value that corresponds to a particular one of one or more fields included in the first dataset. The data monitoring system may then determine whether the first dataset matches a previously analyzed dataset. For example, in some embodiments, data monitoring system may compare the first encode value to a previous encode value that corresponds to a second field of the previously analyzed dataset. Based on this comparison, the data monitoring system may generate an output value that is indicative of a similarity between the first encode value and the previous encode value. The data monitoring system may then determine whether the first dataset matches the previously analyzed dataset based on this output value.

Systems, apparatuses, and methods related to bit string compression are described. A method for bit string compression can include determining that a particular operation is to be performed using a bit string formatted according to a universal number format or a posit format to alter a bit width associated with the bit string from a first bit width to a second bit width and performing a compression operation on a bit string formatted according to a universal number format or a posit format to alter a bit width associated with the bit string from a first bit width to a second bit width. The method can further include writing the bit string having the second bit width to a first register, performing an arithmetic operation or a logical operation, or both using the bit string having the second bit string width, and monitoring a quantity of bits of a result of the operation.

A computer-implemented method for employing input-conditioned filters to perform natural language processing tasks using a convolutional neural network architecture includes receiving one or more inputs, generating one or more sets of filters conditioned on respective ones of the one or more inputs by implementing one or more encoders to encode the one or more inputs into one or more respective hidden vectors, and implementing one or more decoders to determine the one or more sets of filters based on the one or more hidden vectors, and performing adaptive convolution by applying the one or more sets of filters to respective ones of the one or more inputs to generate one or more representations.