Abstract: An apparatus is provided which comprises: a multi-bit quad latch with an internally coupled level sensitive scan circuitry; and a combinational logic coupled to an output of the multi-bit quad latch. Another apparatus is provided which comprises: a plurality of sequential logic circuitries; and a clocking circuitry comprising inverters, wherein the clocking circuitry is shared by the plurality of sequential logic circuitries.

Abstract: A binary CIM circuit enables all memory cells in a memory array to be effectively accessible simultaneously for computation using fixed pulse widths on the wordlines and equal capacitance on the bitlines. The fixed pulse widths and equal capacitance ensure that a minimum voltage drop in the bitline represents one least significant bit (LSB) so that the bitline voltage swing remains safely within the maximum allowable range. The binary CIM circuit maximizes the effective memory bandwidth of a memory array for a given maximum voltage range of bitline voltage.

Abstract: An apparatus is formed. The apparatus includes a stack of semiconductor chips. The stack of semiconductor chips includes a logic chip and a memory stack, wherein, the logic chip includes at least one of a GPU and CPU. The apparatus also includes a semiconductor chip substrate. The stack of semiconductor chips are mounted on the semiconductor chip substrate. At least one other logic chip is mounted on the semiconductor chip substrate. The semiconductor chip substrate includes wiring to interconnect the stack of semiconductor chips to the at least one other logic chip.

Abstract: A loaded capacitance static random-access memory (C-SRAM) is provided. The C-SRAM is configured to prevent full bit line discharge during a functional reads even where the number of bit cells on the bit lines is small. The C-SRAM includes one or more loaded capacitance structures that may take any of a variety of physical configurations designed to provide additional capacitance to the bit lines. For instance, the loaded capacitance structures may take the form of a MIM capacitor in which a ferroelectric layer is formed from one or more high K materials. In addition, the loaded capacitance structures may be positioned in a variety of locations within the C-SRAM, including the back end of line.

Abstract: Techniques are provided for efficient matrix multiplication using in-memory analog parallel processing, with applications for neural networks and artificial intelligence processors. A methodology implementing the techniques according to an embodiment includes storing two matrices in-memory. The first matrix is stored in transposed form such that the transposed first matrix has the same number of rows as the second matrix. The method further includes reading columns of the matrices from the memory in parallel, using disclosed bit line functional read operations and cross bit line functional read operations, which are employed to generate analog dot products between the columns. Each of the dot products corresponds to an element of the matrix multiplication product of the two matrices. In some embodiments, one of the matrices may be used to store neural network weighting factors, and the other matrix may be used to store input data to be processed by the neural network.

Abstract: Embodiments herein describe techniques for a semiconductor device including a memory cell. The memory cell includes a storage cell and a capacitor having a first electrode and a second electrode. The first electrode and the second electrode may be placed in a metal layer below a metal electrode coupled to one or more transistors of the storage cell. The storage cell is to store a digital value, where a voltage value of an output line of the storage cell is to be determined based on the digital value stored in the storage cell. The second electrode of the capacitor is coupled to the output line of the storage cell. The capacitor is to store a charge based on the voltage value of the output line of the storage cell. Other embodiments may be described and/or claimed.

Abstract: A DIMA semiconductor structure is disclosed. The DIMA semiconductor structure includes a frontend including a semiconductor substrate, a transistor switch of a memory cell coupled to the semiconductor substrate and a computation circuit on the periphery of the frontend coupled to the semiconductor substrate. Additionally, the DIMA includes a backend that includes an RRAM component of the memory cell that is coupled to the transistor switch.

Abstract: Embodiments herein describe techniques for a semiconductor device including a RRAM memory cell. The RRAM memory cell includes a FinFET transistor and a RRAM storage cell. The FinFET transistor includes a fin structure on a substrate, where the fin structure includes a channel region, a source region, and a drain region. An epitaxial layer is around the source region or the drain region. A RRAM storage stack is wrapped around a surface of the epitaxial layer. The RRAM storage stack includes a resistive switching material layer in contact and wrapped around the surface of the epitaxial layer, and a contact electrode in contact and wrapped around a surface of the resistive switching material layer. The epitaxial layer, the resistive switching material layer, and the contact electrode form a RRAM storage cell. Other embodiments may be described and/or claimed.

Abstract: Embodiments herein describe techniques for a semiconductor device including a RRAM memory cell. The RRAM memory cell includes a substrate, a RRAM storage cell above the substrate, and a diode adjacent to the RRAM storage cell. The RRAM storage cell includes a first electrode located in a first metal layer above the substrate, a resistive switching material layer adjacent to the first electrode, and a second electrode adjacent to the resistive switching material layer. The second electrode is shared between the RRAM storage cell and the diode. The diode includes the second electrode shared with the RRAM storage cell, a semiconductor layer adjacent to the second electrode, and a third electrode located in a second metal layer above the substrate. Other embodiments may be described and/or claimed.

Abstract: A compute near memory binary neural network accelerator with digital circuits that achieves energy efficiencies comparable to or surpassing a compute near memory binary neural network accelerator with analog circuits is provided. The compute near memory binary neural network accelerator with digital circuits is more process scalable, robust to process, voltage and temperature variations, and immune to circuit noise.

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 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: 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 provided which comprises: a multi-bit quad latch with an internally coupled level sensitive scan circuitry; and a combinational logic coupled to an output of the multi-bit quad latch. Another apparatus is provided which comprises: a plurality of sequential logic circuitries; and a clocking circuitry comprising inverters, wherein the clocking circuitry is shared by the plurality of sequential logic circuitries.

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: Disclosed aspects relate to message management utilizing a social networking environment. A message which pertains to an anticipated activity may be detected in the social networking environment. A relevant actionable portion of the message which corresponds with the anticipated activity may be identified by analyzing the message. Using the relevant actionable portion of the message, an activity management operation for utilization to manage the anticipated activity may be determined. The activity management operation may be executed in the social networking environment for utilization to manage the anticipated activity.

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 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.