In some examples, memristive dot product circuit based floating point computations may include ascertaining a matrix and a vector including floating point values, and partitioning the matrix into a plurality of sub-matrices according to a size of a plurality of memristive dot product circuits. For each sub-matrix of the plurality of sub-matrices, the floating point values may be converted to fixed point values. Based on the conversion and selected ones of the plurality of memristive dot product circuits, a dot product operation may be performed with respect to a sub-matrix and the vector. Each ones of the plurality of memristive dot product circuits may include rows including word line voltages corresponding to the floating point values of the vector, conductances corresponding to the floating point values of an associated sub-matrix, and columns that include bitline currents corresponding to dot products of the voltages and conductances.

A weight cell and device are herein disclosed. The weight cell includes a first field effect transistor (FET) and a first resistive memory element connected to a drain of the first FET, a second FET and a second resistive memory element connected to a drain of the second FET, the drain of the first FET being connected to a gate of the second FET and the drain of the second FET is connected to a gate of the first FET, a third FET and a third resistive memory element connected to a drain of the third FET, and a fourth FET and a fourth resistive memory element connected to a drain of the fourth FET, the drain of the third FET is connected to a gate of the fourth FET and the drain of the fourth FET being connected to a gate of the third FET.

An innovative low-bit-width device may include a first digital-to-analog converter (DAC), a second DAC, a plurality of non-volatile memory (NVM) weight arrays, one or more analog-to-digital converters (ADCs), and a neural circuit. The first DAC is configured to convert a digital input signal into an analog input signal. The second DAC is configured to convert a digital previous hidden state (PHS) signal into an analog PHS signal. NVM weight arrays are configured to compute vector matrix multiplication (VMM) arrays based on the analog input signal and the analog PHS signal. The NVM weight arrays are coupled to the first DAC and the second DAC. The one or more ADCs are coupled to the plurality of NVM weight arrays and are configured to convert the VMM arrays into digital VMM values. The neural circuit is configured to process the digital VMM values into a new hidden state.

A compute-in-memory array is provided that includes a set of compute-in-memory bitcells that time share a shared capacitor connected between the set of compute-in-memory bitcells and a read bit line.

A nonvolatile memory device includes a memory cell array, an input current generator, an operation cell array and an analog-to-digital converter. The memory cell array includes NAND strings storing multiplicand data, wherein first ends of the NAND strings are connected to bitlines and second ends of the NAND strings output multiplication bits corresponding to bitwise multiplication of the multiplicand data stored in the NAND strings and multiplier data loaded on the bitlines. The input current generator generates input currents. The operation cell array includes switching transistors. Gate electrodes of the switching transistors are connected to the second ends of the NAND strings. The switching transistors selectively sum the input currents based on the multiplication bits to output the output currents. The analog-to-digital converter converts the output currents to digital values.

In-memory computing architectures and methods of performing multiply-and-accumulate operations are provided. The method includes sequentially shifting bits of first input bytes into each row in an array of memory cells arranged in rows and columns. Each memory cell is activated based on the bit to produce a bit-line current from each activated memory cell in a column on a shared bit-line proportional to a product of the bit and a weight stored therein. Charges produced by a sum of the bit-line currents in a column are accumulated in first charge-storage banks coupled to a shared bit-line in each of the columns. Concurrently, charges from second input bytes accumulated in second charge-storage banks previously coupled to the columns are sequentially converted into output bytes. The charge-storage banks are exchanged after the first input bytes have been accumulated and the charges from the second input bytes converted. The method then repeats.

In a computing-in-memory chip and a memory cell array structure, a memory cell array therein includes a plurality of memory cell sub-arrays arranged in an array. Each memory cell sub-array comprises a plurality of switch units and a plurality of memory cells arranged in an array; and first terminals of all memory cells in each column are connected to a source line, second terminals of all the memory cells are connected to a bit line, third terminals of all memory cells in each row are connected to a word line through a switch unit, a plurality of rows of memory cells are correspondingly connected to a plurality of switch units, control terminals of the plurality of switch units are connected to a local word line of the memory cell sub-array, and whether to activate the memory cell sub-array is controlled by controlling the local word line.

Disclosed are systems and methods for performing efficient vector-matrix multiplication using a sparsely-connected conductance matrix and analog mixed signal (AMS) techniques. Metal electrodes are sparsely connected using coaxial nanowires. Each electrode can be used as an input/output node or neuron in a neural network layer. Neural network synapses are created by random connections provided by coaxial nanowires. A subset of the metal electrodes can be used to receive a vector of input voltages and the complementary subset of the metal electrodes can be used to read output currents. The output currents are the result of vector-matrix multiplication of the vector of input voltages with the sparsely-connected matrix of conductances.

A nonvolatile memory device includes a memory cell array, an input current generator, an operation cell array and an analog-to-digital converter. The memory cell array includes NAND strings storing multiplicand data, wherein first ends of the NAND strings are connected to bitlines and second ends of the NAND strings output multiplication bits corresponding to bitwise multiplication of the multiplicand data stored in the NAND strings and multiplier data loaded on the bitlines. The input current generator generates input currents. The operation cell array includes switching transistors. Gate electrodes of the switching transistors are connected to the second ends of the NAND strings. The switching transistors selectively sum the input currents based on the multiplication bits to output the output currents. The analog-to-digital converter converts the output currents to digital values.

A method, system and electronic device for mitigating variance in a two transistor two resistive memory element (2T2R) circuit is provided. The method includes calculating a sum of a number of logical 1's in a column of bitcells in the 2T2R circuit, N, of an input vector, sensing output current values from each current line in the column of bitcells and calculating an inner product, M, of the input vector and the bitcells in the column in the 2T2R circuit based on the sensed output current values