The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as management of data among the various memory components of the NN/DNN. Using virtualized hardware iterators, data for processing by the NN/DNN can be traversed and configured to optimize the number of operations as well as memory utilization to enhance the overall performance of a NN/DNN. Operatively, an iterator controller can generate instructions for execution by the NN/DNN representative of one more desired iterator operation types and to perform one or more iterator operations. Data can be iterated according to a selected iterator operation and communicated to one or more neuron processors of the NN/DD for processing and output to a destination memory. The iterator operations can be applied to various volumes of data (e.g., blobs) in parallel or multiple slices of the same volume.

The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as management of data among the various memory components of the NN/DNN. Using virtualized hardware iterators, data for processing by the NN/DNN can be traversed and configured to optimize the number of operations as well as memory utilization to enhance the overall performance of a NN/DNN. Operatively, an iterator controller can generate instructions for execution by the NN/DNN representative of one more desired iterator operation types and to perform one or more iterator operations. Data can be iterated according to a selected iterator operation and communicated to one or more neuron processors of the NN/DD for processing and output to a destination memory. The iterator operations can be applied to various volumes of data (e.g., blobs) in parallel or multiple slices of the same volume.

A method for providing in-place safe decompression of a data stream includes utilizing a stored offset to allocate a memory space for a decompression operation. The stored offset represents a maximum offset by which a write pointer position of an output stream exceeds a read pointer position for a corresponding input stream during in-place decompression of the compressed data stream.

A method for providing in-place safe decompression of a data stream includes utilizing a stored offset to allocate a memory space for a decompression operation. The stored offset represents a maximum offset by which a write pointer position of an output stream exceeds a read pointer position for a corresponding input stream during in-place decompression of the compressed data stream.

An exemplary computing environment having a DNN module can maintain one or more bandwidth throttling mechanisms. Illustratively, a first throttling mechanism can specify the number of cycles to wait between transactions on a cooperating fabric component (e.g., data bus). Illustratively, a second throttling mechanism can be a transaction count limiter that operatively sets a threshold of a number of transactions to be processed during a given transaction sequence and limits the number of transactions such as multiple transactions in flight to not exceed the set threshold. In an illustrative operation, in executing these two exemplary calculated throttling parameters, the average bandwidth usage and the peak bandwidth usage can be limited. Operatively, with this fabric bandwidth control, the processing units of the DNN are optimized to process data across each transaction cycle resulting in enhanced processing and lower power consumption.

Described is an apparatus and method for data compression using compressive sensing in a wearable device. Described is also a machine-readable storage media having instruction stored thereon, that when executed, cause one or more processors to perform an operation comprising: receive an input signal from a sensor; convert the input signal to a digital stream; and symmetrically pad on either ends of the digital stream with a portion of the digital stream to form a padded digital stream.

Embodiments of the invention are directed to a DEFLATE compression accelerator and to a method for reducing a latch count required for symbol sorting when generating a dynamic Huffman table. The accelerator includes an input buffer and a Lempel-Ziv 77 (LZ77) compressor communicatively coupled to an output of the input buffer. The accelerator further includes a Huffman encoder communicatively coupled to the LZ77 compressor. The Huffman encoder includes a bit translator. The accelerator further includes an output buffer communicatively coupled to the Huffman encoder.

Embodiments of the invention are directed to a DEFLATE compression accelerator and to a method for reducing a latch count required for symbol sorting when generating a dynamic Huffman table. The accelerator includes an input buffer and a Lempel-Ziv 77 (LZ77) compressor communicatively coupled to an output of the input buffer. The accelerator further includes a Huffman encoder communicatively coupled to the LZ77 compressor. The Huffman encoder includes a bit translator. The accelerator further includes an output buffer communicatively coupled to the Huffman encoder.

Data records may be managed in a relational database by monitoring, a record length for a first data record in a page of memory, an amount of free space in the page, and a page length. In response to receiving an operator command to replace the first data record with a second data record, a database management system may determine whether an estimated record length of a compressed second data record is outside of the amount of free space in the page. In response to determining the estimated record length of a compressed second data record is outside of the amount of free space in the page, the database management system may determine whether an estimated length of a compressed page is outside of the page length. In response to determining the estimated length of a compressed page is within the page length, the page may be compressed.

An electronic device for compressing sampled data comprises a memory element and a processing element. The memory element is configured to store sampled data points and sampled times. The processing element is in electronic communication with the memory element and is configured to receive a plurality of sampled data points, a slope for each sampled data point in succession, the slope being a value of a change between the sampled data point and its successive sampled data point, and store the sampled data point in the memory element when the slope changes in value from a previous sampled data point.