Data processing apparatuses, methods of data processing, complementary instructions and programs related to ring buffer administration are disclosed. An enqueuing operation performs an atomic compare-and-swap oper-ation to store a first processed data item indication to an enqueuing-target slot in the ring buffer contingent on an in-order marker not being present there and, when successful, determines that a ready-to-dequeue condition is true for the first processed data item indication. A dequeuing operation, when the ready-to-de-queue condition for a dequeuing-target slot is true, comprises writing a null data item to the dequeuing-target slot and, when dequeuing in-order, further comprises, dependent on whether a next contiguous slot has null content, determining a retirement condition and, when the retirement condition is true, performing a retirement process on the next contiguous slot comprising making the next con-tiguous slot available to a subsequent enqueuing operation. Further subsequent slots may also be retired.

Some embodiments of the present disclosure provide an associatively indexed circular buffer (ACB). The ACB may be viewed as a dynamically allocatable memory structure that offers in-order data access (say, first-in-first-out, or “FIFO”) or random order data access at a fixed, relatively low latency. The ACB includes a data store of non-contiguous storage. To manage the pushing of data to, and popping data from, the data store, the ACB includes a contiguous pointer generator, a content addressable memory (CAM) and a free pool.

A method of storing information using monomers such as nucleotides is provided including converting a format of information into a plurality of bit sequences of a bit stream with each having a corresponding bit barcode, converting the plurality of bit sequences to a plurality of corresponding oligonucleotide sequences using one bit per base encoding, synthesizing the plurality of corresponding oligonucleotide sequences on a substrate having a plurality of reaction locations, and storing the synthesized plurality of corresponding oligonucleotide sequences.

A method of storing information using monomers such as nucleotides is provided including converting a format of information into a plurality of bit sequences of a bit stream with each having a corresponding bit barcode, converting the plurality of bit sequences to a plurality of corresponding oligonucleotide sequences using one bit per base encoding, synthesizing the plurality of corresponding oligonucleotide sequences on a substrate having a plurality of reaction locations, and storing the synthesized plurality of corresponding oligonucleotide sequences.

Enhanced techniques and circuitry are presented herein for artificial neural networks. These artificial neural networks are formed from artificial synapses, which in the implementations herein comprise a memory arrays having non-volatile memory elements. In one implementation, an apparatus comprises a plurality of non-volatile memory arrays configured to store weight values for an artificial neural network. Each of the plurality of non-volatile memory arrays can be configured to receive data from a unified buffer shared among the plurality of non-volatile memory arrays, operate on the data, and shift at least portions of the data to another of the plurality of non-volatile memory arrays.

Enhanced techniques and circuitry are presented herein for artificial neural networks. These artificial neural networks are formed from artificial synapses, which in the implementations herein comprise a memory arrays having non-volatile memory elements. In one implementation, an apparatus comprises a plurality of non-volatile memory arrays configured to store weight values for an artificial neural network. Each of the plurality of non-volatile memory arrays can be configured to receive data from a unified buffer shared among the plurality of non-volatile memory arrays, operate on the data, and shift at least portions of the data to another of the plurality of non-volatile memory arrays.

Methods and apparatus for synchronizing data transfers across clock domains for using heads-up indications. An integrated circuit includes a first-in first-out buffer (FIFO); a memory controller configured to operate in a first clock domain and coupled to the FIFO, the first clock domain associated with a first clock signal; a data fabric configured to operate in a second clock domain and coupled to the FIFO, the second clock domain associated with a second clock signal, a second frequency of the second clock signal being different from a first frequency of the first clock signal; and a controller coupled to the FIFO. In some instances, the controller determines a phase relationship between the first clock signal and the second clock signal; monitors one or more first clock edges of the first clock signal and one or more second clock edges of the second clock signal; and sends a first heads-up signal to the memory controller.

Methods and apparatus for synchronizing data transfers across clock domains for using heads-up indications. An integrated circuit includes a first-in first-out buffer (FIFO); a memory controller configured to operate in a first clock domain and coupled to the FIFO, the first clock domain associated with a first clock signal; a data fabric configured to operate in a second clock domain and coupled to the FIFO, the second clock domain associated with a second clock signal, a second frequency of the second clock signal being different from a first frequency of the first clock signal; and a controller coupled to the FIFO. In some instances, the controller determines a phase relationship between the first clock signal and the second clock signal; monitors one or more first clock edges of the first clock signal and one or more second clock edges of the second clock signal; and sends a first heads-up signal to the memory controller.

A circuit for performing neural network computations for a neural network comprising a plurality of layers, the circuit comprising: activation circuitry configured to receive a vector of accumulated values and configured to apply a function to each accumulated value to generate a vector of activation values; and normalization circuitry coupled to the activation circuitry and configured to generate a respective normalized value from each activation value.

A circuit for performing neural network computations for a neural network comprising a plurality of layers, the circuit comprising: activation circuitry configured to receive a vector of accumulated values and configured to apply a function to each accumulated value to generate a vector of activation values; and normalization circuitry coupled to the activation circuitry and configured to generate a respective normalized value from each activation value.