Apparatus and methods are disclosed for throttling processor operation in block-based processor architectures. In one example of the disclosed technology, a block-based instruction set architecture processor includes a plurality of processing cores configured to fetch and execute a sequence of instruction blocks. Each of the processing cores includes function resources for performing operations specified by the instruction blocks. The processor further includes a core scheduler configured to allocate functional resources for performing the operations. The functional resources are allocated for executing the instruction blocks based, at least in part, on a performance metric. The performance metric can be generated dynamically or statically based on branch prediction accuracy, energy usage tolerance, and other suitable metrics.

An apparatus and method for scalable qubit addressing. For example, one embodiment of a processor comprises: a decoder comprising quantum instruction decode circuitry to decode quantum instructions to generate quantum microoperations (uops) and non-quantum decode circuitry to decode non-quantum instructions to generate non-quantum uops; execution circuitry comprising: an address generation unit (AGU) to generate a system memory address responsive to execution of one or more of the non-quantum uops; and quantum index generation circuitry to generate quantum index values responsive to execution of one or more of the quantum uops, each quantum index value uniquely identifying a quantum bit (qubit) in a quantum processor; wherein to generate a first quantum index value for a first quantum uop, the quantum index generation circuitry is to read the first quantum index value from a first architectural register identified by the first quantum uop.

A quantum computing device comprises a surface code lattice that includes l logical qubits, where l is a positive integer. The surface code lattice is partitioned into two or more regions based on lattice geometry. A compression engine is coupled to each logical qubit of the l logical qubits. Each compression engine is configured to compress syndrome data generated by the surface code lattice using a geometry-based compression scheme. A decompression engine is coupled to each compression engine. Each decompression engine is configured to receive compressed syndrome data, decompress the received compressed syndrome data, and route the decompressed syndrome data to a decoder block.

A prefetcher for a coprocessor is disclosed. An apparatus includes a processor and a coprocessor that are configured to execute processor and coprocessor instructions, respectively. The processor and coprocessor instructions appear together in code sequences fetched by the processor, with the coprocessor instructions being provided to the coprocessor by the processor. The apparatus further includes a coprocessor prefetcher configured to monitor a code sequence fetched by the processor and, in response to identifying a presence of coprocessor instructions in the code sequence, capture the memory addresses, generated by the processor, of operand data for coprocessor instructions. The coprocessor is further configured to issue, for a cache memory accessible to the coprocessor, prefetches for data associated with the memory addresses prior to execution of the coprocessor instructions by the coprocessor.

A quantum computing device comprises a surface code lattice that includes l logical qubits, where l is a positive integer. The surface code lattice is partitioned into two or more regions based on lattice geometry. A compression engine is coupled to each logical qubit of the l logical qubits. Each compression engine is configured to compress syndrome data generated by the surface code lattice using a geometry-based compression scheme. A decompression engine is coupled to each compression engine. Each decompression engine is configured to receive compressed syndrome data, decompress the received compressed syndrome data, and route the decompressed syndrome data to a decoder block.

Techniques are disclosed relating to an apparatus that includes a plurality of execution pipelines including first and second execution pipelines, a shared circuit that is shared by the first and second execution pipelines, and a decode circuit. The first and second execution pipelines are configured to concurrently perform operations for respective instructions. The decode circuit is configured to assign a first program thread to the first execution pipeline and a second program thread to the second execution pipeline. In response to determining that respective instructions from the first and second program threads that utilize the shared circuit are concurrently available for dispatch, the decode circuit is further configured to select between the first program thread and the second program thread.

Embodiments described herein provide for an instruction and associated logic to enable a vector multiply add instructions with automatic zero skipping for sparse input. One embodiment provides for a general-purpose graphics processor comprising logic to perform operations comprising fetching a hardware macro instruction having a predicate mask, a repeat count, and a set of initial operands, where the initial operands include a destination operand and multiple source operands. The hardware macro instruction is configured to perform one or more multiply/add operations on input data associated with a set of matrices.

Methods of encoding and decoding are described which use a variable number of instruction words to encode instructions from an instruction set, such that different instructions within the instruction set may be encoded using different numbers of instruction words. To encode an instruction, the bits within the instruction are re-ordered and formed into instruction words based upon their variance as determined using empirical or simulation data. The bits in the instruction words are compared to corresponding predicted values and some or all of the instruction words that match the predicted values are omitted from the encoded instruction.

Disclosed embodiments relate to instructions for fused multiply-add (FMA) operations with variable-precision inputs. In one example, a processor to execute an asymmetric FMA instruction includes fetch circuitry to fetch an FMA instruction having fields to specify an opcode, a destination, and first and second source vectors having first and second widths, respectively, decode circuitry to decode the fetched FMA instruction, and a single instruction multiple data (SIMD) execution circuit to process as many elements of the second source vector as fit into an SIMD lane width by multiplying each element by a corresponding element of the first source vector, and accumulating a resulting product with previous contents of the destination, wherein the SIMD lane width is one of 16 bits, 32 bits, and 64 bits, the first width is one of 4 bits and 8 bits, and the second width is one of 1 bit, 2 bits, and 4 bits.

Examples of the present disclosure relate to an apparatus comprising execution circuitry to execute instructions defining data processing operations on data items. The apparatus comprises cache storage to store temporary copies of the data items. The apparatus comprises prefetching circuitry to a) predict that a data item will be subject to the data processing operations by the execution circuitry by determining that the data item is consistent with an extrapolation of previous data item retrieval by the execution circuitry, and identifying that at least one control flow element of the instructions indicates that the data item will be subject to the data processing operations by the execution circuitry; and b) prefetch the data item into the cache storage.