An integrated-circuit neural network includes chain of multiply-accumulate units co-located with a high-bandwidth storage array. Each multiply accumulate includes a digital input port, analog input port and multiply-adder circuitry. The digital input port receives a matrix of digital-weight values from the storage array and the analog input port receives a counterpart matrix of analog input signals, each analog input signal exhibiting a respective electronic current representative of input value. The multiply-adder circuitry generates a matrix of analog output signals by convolving the matrix of digital-weight values with the matrix of analog input signals including, for each analog output signal within the matrix of analog output signals, switchably enabling weighted current contributions to the analog output signal based on logic states of on respective bits of one or more of the digital-weight values.

A mixed-signal integrated circuit that includes: a global reference signal source; a first summation node and a second summation node; a plurality of distinct pairs of current generating circuits arranged along the first summation node and the second summation node; a first current generating circuit of each of the plurality of distinct pairs that is arranged on the first summation node and a second current generating circuit of each of the plurality of distinct pairs is arranged on the second summation node; a common-mode current circuit that is arranged in electrical communication with each of the first and second summation nodes; where a local DAC adjusts a differential current between the first second summation nodes based on reference signals from the global reference source; and a comparator or a finite state machine that generates a binary output value current values obtained from the first and second summation nodes.

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.

A differential mixed-signal logic processor is provided. The differential mixed-signal logic processor includes a plurality of mixed-signal multiplier branches for multiplication of an analog value A and a N-bit digital value B. Each of the plurality of mixed-signal multiplier branches include a first capacitor connected across a second capacitor and a third capacitor to provide a differential output across the second and third capacitors. A capacitance of the first capacitor is equal to half a capacitance of the second and third capacitors.

A differential mixed-signal logic processor is provided. The differential mixed-signal logic processor includes a plurality of mixed-signal multiplier branches for multiplication of an analog value A and a N-bit digital value B. Each of the plurality of mixed-signal multiplier branches include a first capacitor connected across a second capacitor and a third capacitor to provide a differential output across the second and third capacitors. A capacitance of the first capacitor is equal to half a capacitance of the second and third capacitors.

A compute cell for in-memory multiplication of a digital data input and a balanced ternary weight, and an in-memory computing device including an array of the compute cells, are provided. In one aspect, the compute cell includes a set of input connectors for receiving modulated input signals representative of a sign and a magnitude of the data input, and a memory unit configured to store the ternary weight. A logic unit connected to the set of input connectors and the memory unit receives the data input and the ternary weight. The logic unit selectively enables one of a plurality of conductive paths for supplying a partial charge to a read bit line during a compound duty cycle of the set of input signals as a function of the respective signs of data input and ternary weight, and disables each of the plurality of conductive paths if at least one of the ternary weight and data input have zero magnitude.

The present disclosure provides methods and systems for using a hybrid architecture of classical and non-classical (e.g., quantum) computing to compute the quantum mechanical energy and/or electronic structure of a chemical system, as well as to identify stable conformations of a chemical system (e.g., a molecule) and/or to perform an ab initio molecular dynamics calculation or simulation on the chemical system.

The present disclosure provides methods and systems for using a hybrid architecture of classical and non-classical (e.g., quantum) computing to compute the quantum mechanical energy and/or electronic structure of a chemical system, as well as to identify stable conformations of a chemical system (e.g., a molecule) and/or to perform an ab initio molecular dynamics calculation or simulation on the chemical system.

The present disclosure relates to a circuit structure for in-memory computing. The circuit structure comprises a plurality of 8T SRAMs, four BLs, two WLs, and a direction configuration circuit. Each of the 8T SRAMs comprises two groups of read/write dual ports, two WL ports and two direction configuration ports. Data of first read/write port and second read/write port of each group of the read/write dual ports are inverse of each other. Each of the BLs is connected to a corresponding processor, and is connected to a read/write port of a corresponding read/write dual port of each 8T SRAM in a row direction or a column direction. Each of the WLs is connected to a corresponding processor and connected to a corresponding WL port of each 8T SRAM.

A differential mixed-signal logic processor is provided. The differential mixed-signal logic processor includes a plurality of mixed-signal multiplier branches for multiplication of an analog value A and a N-bit digital value B. Each of the plurality of mixed-signal multiplier branches include a first capacitor connected across a second capacitor and a third capacitor to provide a differential output across the second and third capacitors. A capacitance of the first capacitor is equal to half a capacitance of the second and third capacitors.