Abstract: In one embodiment, an apparatus comprises a multiplier circuit to: identify a plurality of partial products associated with a multiply operation; partition the plurality of partial products into a first set of partial products, a second set of partial products, and a third set of partial products; determine whether the multiply operation is associated with a square operation; upon a determination that the multiply operation is associated with the square operation, compute a result based on the first set of partial products and the third set of partial products; and upon a determination that the multiply operation is not associated with the square operation, compute the result based on the first set of partial products, the second set of partial products, and the third set of partial products.

Abstract: Techniques and mechanisms for providing data to be used in an in-memory computation at a memory device. In an embodiment a memory device comprises a first memory array and circuitry, coupled to the first memory array, to perform a data computation based on data stored at the first memory array. Prior to the computation, the first memory array receives the data from a second memory array of the memory device. The second memory array extends horizontally in parallel with, but is offset vertically from, the first memory array. In another embodiment, a single integrated circuit die includes both the first memory array and the second memory array.

Abstract: A compute near memory (CNM) convolution accelerator enables a convolutional neural network (CNN) to use dedicated acceleration to achieve efficient in-place convolution operations with less impact on memory and energy consumption. A 2D convolution operation is reformulated as 1D row-wise convolution. The 1D row-wise convolution enables the CNM convolution accelerator to process input activations row-by-row, while using the weights one-by-one. Lightweight access circuits provide the ability to stream both weights and input row as vectors to MAC units, which in turn enables modules of the CNM convolution accelerator to implement convolution for both [1×1] and chosen [n×n] sized filters.

Abstract: A memory circuit includes a number (X) of multiply-accumulate (MAC) circuits that are dynamically configurable. The MAC circuits can either compute an output based on computations of X elements of the input vector with the weight vector, or to compute the output based on computations of a single element of the input vector with the weight vector, with each element having a one bit or multibit length. A first memory can hold the input vector having a width of X elements and a second memory can store the weight vector. The MAC circuits include a MAC array on chip with the first memory.

Abstract: An apparatus is described. The apparatus includes a circuit to process a binary neural network. The circuit includes an array of processing cores, wherein, processing cores of the array of processing cores are to process different respective areas of a weight matrix of the binary neural network. The processing cores each include add circuitry to add only those weights of an i layer of the binary neural network that are to be effectively multiplied by a non zero nodal output of an i?1 layer of the binary neural network.

Abstract: An apparatus is described. The apparatus includes a long short term memory (LSTM) circuit having a multiply accumulate circuit (MAC). The MAC circuit has circuitry to rely on a stored product term rather than explicitly perform a multiplication operation to determine the product term if an accumulation of differences between consecutive, preceding input values has not reached a threshold.

Abstract: Computing devices and techniques for performing modular exponentiation for a data encryption process are described. In one embodiment, for example, an apparatus may include at least one memory logic for an encryption unit to perform encryption according to RSA encryption using a parallel reduction multiplier (PRM) MM process, at least a portion of the logic comprised in hardware coupled to the at least one memory and the at least one wireless transmitter, the logic to precompute a reduction coefficient, determine an operand product and a reduction product in parallel, the reduction product based on the reduction coefficient, and generate a MM result for the PRM MM process based on the operand product and the reduction product. Other embodiments are described and claimed.

Abstract: Embodiments are directed to post quantum public key signature operation for reconfigurable circuit devices. An embodiment of an apparatus includes one or more processors; and a reconfigurable circuit device, the reconfigurable circuit device including a dedicated cryptographic hash hardware engine, and a reconfigurable fabric including logic elements (LEs), wherein the one or more processors are to configure the reconfigurable circuit device for public key signature operation, including mapping a state machine for public key generation and verification to the reconfigurable fabric, including mapping one or more cryptographic hash engines to the reconfigurable fabric, and combining the dedicated cryptographic hash hardware engine with the one or more mapped cryptographic hash engines for cryptographic signature generation and verification.

Abstract: In one example an apparatus comprises a computer readable memory, hash logic to generate a message hash value based on an input message, signature logic to generate a signature to be transmitted in association with the message, the signature logic to apply a hash-based signature scheme to a private key to generate the signature comprising a public key, and accelerator logic to pre-compute at least one set of inputs to the signature logic. Other examples may be described.

Abstract: In one example an apparatus comprises a computer readable memory, an XMSS operations logic to manage XMSS functions, a chain function controller to manage chain function algorithms, a secure hash algorithm-2 (SHA2) accelerator, a secure hash algorithm-3 (SHA3) accelerator, and a register bank shared between the SHA2 accelerator and the SHA3 accelerator. Other examples may be described.

Abstract: In one example an apparatus comprises a computer readable memory, an XMSS verification manager logic to manage XMSS verification functions, a one-time signature and public key generator logic, a chain function logic to implement chain function algorithms, a low latency SHA3 hardware engine, and a register bank communicatively coupled to the XMSS verification manager logic. Other examples may be described.

Abstract: A mechanism is described for facilitating unified accelerator for classical and post-quantum digital signature schemes in computing environments, according to one embodiment. A method of embodiments, as described herein, includes unifying classical cryptography and post-quantum cryptography through a unified hardware accelerator hosted by a trusted platform of the computing device. The method may further include facilitating unification of a first finite state machine associated with the classical cryptography and a second finite state machine associated with the post-quantum cryptography though one or more of a single the hash engine, a set of register file banks, and a modular exponentiation engine.

Abstract: In one example an apparatus comprises a computer readable memory, a signature logic to generate a signature to be transmitted in association with a message, the signature logic to apply a hash-based signature scheme to the message using a private key to generate the signature comprising a public key, or a verification logic to verify a signature received in association with the message, the verification logic to apply the hash-based signature scheme to verify the signature using the public key, and an accelerator logic to apply a structured order to at least one set of inputs to the hash-based signature scheme. Other examples may be described.

Abstract: In one example an apparatus comprises a computer-readable memory, signature logic to compute a message hash of an input message using a secure hash algorithm, process the message hash to generate an array of secret key components for the input message, apply a hash chain function to the array of secret key components to generate an array of signature components, the hash chain function comprising a series of even-index hash chains and a series of odd-index hash chains, wherein the even-index hash chains and the odd-index hash chains generate a plurality of intermediate node values and a one-time public key component between the secret key components and the signature components and store at least some of the intermediate node values in the computer-readable memory for use in one or more subsequent signature operations. Other examples may be described.

Abstract: Examples described herein relate to a neural network whose weights from a matrix are selected from a set of weights stored in a memory on-chip with a processing engine for generating multiply and carry operations. The number of weights in the set of weights stored in the memory can be less than a number of weights in the matrix thereby reducing an amount of memory used to store weights in a matrix. The weights in the memory can be generated in training using gradients from back propagation. Weights in the memory can be selected using a tabulation hash calculation on entries in a table.

Abstract: An apparatus and method are described for a neuromorphic processor design in which neuron timing information is duplicated on a neuromorphic core.

Abstract: A neuromorphic computing system is provided which comprises: a synapse core; and a pre-synaptic neuron, a first post-synaptic neuron, and a second post-synaptic neuron coupled to the synaptic core, wherein the synapse core is to: receive a request from the pre-synaptic neuron, generate, in response to the request, a first address of the first post-synaptic neuron and a second address of the second post-synaptic neuron, wherein the first address and the second address are not stored in the synapse core prior to receiving the request.

Abstract: Techniques and mechanisms for providing data to be used in an in-memory computation at a memory device. In an embodiment a memory device comprises a first memory array and circuitry, coupled to the first memory array, to perform a data computation based on data stored at the first memory array. Prior to the computation, the first memory array receives the data from a second memory array of the memory device. The second memory array extends horizontally in parallel with, but is offset vertically from, the first memory array. In another embodiment, a single integrated circuit die includes both the first memory array and the second memory array.

Abstract: Systems and methods may apply homeostatic plasticity control in a spiking neural network, such as at a neuron of a core of the spiking neural network. The neuron may receive input spike information and determine whether to activate an output spike at the neuron based at least in part on the input spike information and a bias value. The bias value may be set based on whether the neuron issued a previous output spike during a previous time period. The bias value may be updated based on whether the output spike was activated at the neuron. For example, in accordance with a determination to activate the output spike, the bias value may be decreased, and in accordance with a determination to not activate the output spike, the bias value may be increased.

Abstract: An apparatus is described. The apparatus includes a compute-in-memory (CIM) circuit for implementing a neural network disposed on a semiconductor chip. The CIM circuit includes a mathematical computation circuit coupled to a memory array. The memory array includes an embedded dynamic random access memory (eDRAM) memory array. Another apparatus is described. The apparatus includes a compute-in-memory (CIM) circuit for implementing a neural network disposed on a semiconductor chip. The CIM circuit includes a mathematical computation circuit coupled to a memory array. The mathematical computation circuit includes a switched capacitor circuit. The switched capacitor circuit includes a back-end-of-line (BEOL) capacitor coupled to a thin film transistor within the metal/dielectric layers of the semiconductor chip. Another apparatus is described. The apparatus includes a compute-in-memory (CIM) circuit for implementing a neural network disposed on a semiconductor chip.