A mixed-signal logic processor is provided. The mixed-signal logic processor includes a plurality of mixed-signal multiplier branches. Each of the plurality of mixed-signal multiplier branches has a set of branch-dedicated switches and a single branch-dedicated capacitor. The mixed-signal logic further includes a common switch. The common switch is external and common to each of the plurality of mixed-signal multiplier branches. The mixed-signal logic also includes a first shared branch-external capacitor and a second shared branch-external capacitor. The first and the second shared branch-external capacitors are external to and shared by each of the plurality of mixed-signal multiplier branches. Various settings of the set of switches and the common switch enable various modes of the mixed-signal dot product processor.

A circuit configured to compute matrix multiply-and-add calculations that includes a digital-to-time converter configured to receive a digital input and output a signal proportional to the digital input and modulated in time-domain associated with a reference time, a memory including a crossbar network, wherein the memory is configured to receive the time modulated signal from the digital-to-time converter and output a weighted signal scaled in response to network weights of the crossbar network and the time modulated input signal, and an output interface in communication with the crossbar network and configured to receive its weighted output signal and output a digital value proportional to at least the reference time using a time-to-digital converter.

The present disclosure provides a multiplication and accumulation circuit based on radix-4 booth code and differential weight storage. The circuit includes an input data encoding circuit, a differential weight storage circuit, an integral calculation circuit and a differential ADC circuit. The input data encoding circuit is configured to encode original input data. The differential weight storage circuit is configured to store weight values, and multiply the original input data after being encoded by the weight values stored to obtain multiplication results. The integral calculation circuit is configured to respectively accumulate a positive value and a negative value of each multiplication result. The differential ADC circuit is configured to perform analog-to-digital conversion on a difference between accumulated results of the positive values and the negative values to obtain a digital multiplication and accumulation result.

A circuit for a neuron of a multi-stage compute process is disclosed. The circuit comprises a weighted charge packet (WCP) generator. The circuit may also include a voltage divider controlled by a programmable resistance component (e.g., a memristor). The WCP generator may also include a current mirror controlled via the voltage divider and arrival of an input spike signal to the neuron. WCPs may be created to represent the multiply function of a multiply accumulate processor. The WCPs may be supplied to a capacitor to accumulate and represent the accumulate function. The value of the WCP may be controlled by the length of the spike in signal times the current supplied through the current mirror. Spikes may be asynchronous. Memristive components may be electrically isolated from input spike signals so their programmed conductance is not affected. Positive and negative spikes and WCPs for accumulation may be supported.

A neural network device includes a shift register circuit, a control circuit, and a processing circuit. The shift register circuit includes registers configured to, in each cycle of cycles, transfer stored data to a next register and store new data received from a previous register to a current register. The control circuit is configured to sequentially input data of input activations included in an input feature map into the shift register circuit in a preset order. The processing circuit, includes crossbar array groups that receive input activations from at least one of the registers and perform a multiply-accumulate (MAC) operation with respect to the received input activation and weights, is configured to accumulate and add at least some operation results output from the crossbar array groups in a preset number of cycles to obtain an output activation in an output feature map.

Systems, apparatuses and methods include technology that receives, with a first plurality of multipliers of a multiply-accumulator (MAC), first digital signals from a memory array, wherein the first plurality of multipliers includes a plurality of capacitors. The technology further executes, with the first plurality of multipliers, multibit computation operations with the plurality of capacitors based on the first digital signals, and generates, with the first plurality of multipliers, a first analog signal based on the multibit computation operations.

A multiply-accumulate calculation device includes: multiple calculation units which generates output signals by multiplying an input signal corresponding to an input value and having a rising part, a signal part, and a falling part by a weight, and output the output signals; an accumulate calculation unit configured to calculate a sum of the output signals output from the plurality of multiple calculation units; and a correction unit configured to execute correction processing for correcting the sum of the output signals on the basis of a correction value including at least one of a first value incorporated into the sum by a current flowing into variable resistors of the multiple calculation units due to the rising part of the input signal, and a second value incorporated into the sum by a current flowing into the variable resistors of the multiple calculation units due to the falling part of the input signal.

An in-memory computation device including a memory array, p×q analog to digital converters (ADCs) and a ladder adder is provided. The memory array is divided into p×q memory tiles, where p and q are positive integers larger than 1. Each of the memory tiles has a plurality local bit lines coupled to a global bit line respectively through a plurality of bit line selection switches. The bit line selection switches are turned on or cur off according to a plurality of control signals. The memory array receives a plurality of input signals. The ADCs are respectively coupled to a plurality of global bit lines of the memory tiles. The ADCs respectively convert electrical signals on the global bit lines to generate a plurality of sub-output signals. The ladder adder is coupled to the ADCs, and performs an addition operation on the sub-output signals to generate a calculation result.

An improved electronic mixed mode multiplier and accumulate circuit for artificial intelligence and computing system applications that perform vector-vector, vector-matrix and other multiply-accumulate computations. The circuit is provided is a high resolution, high linearity, low area, low power multiply—accumulate (MAC) unit to interface with a memory device for storing computation output results. The MAC unit uses a less number of current carrying elements resulting in much lower integrated circuit area, and provides a tight matching between the current elements thus preserving inherent linearity requirements due to current mode operation. Further the MAC performs current scaling using switches and current division where the current switches occupy minimum size transistors requiring a small area to implement that renders it compatible with MRAM such as a magnetic tunnel junction device. The MAC is hierarchically extended for increased number of bits to provide a delay implementation using orthogonal vector and current addition.

In a compute-in-memory (“CIM”) system, current signals, indicative of the result of a multiply-and-accumulate operation, from a CIM memory circuit are computed by comparing them with reference currents, which are generated by a current digital-to-analog converter (“DAC”) circuit. The memory circuit can include non-volatile memory (“NVM”) elements, which can be multi-level or two-level NVM elements. The characteristic sizes of the memory elements can be binary weighted to correspond to the respective place values in a multi-bit weight and/or a multi-bit input signal. Alternatively, NVM elements of equal size can be used to drive transistors of binary weighted sizes. The current comparison operation can be carried out at higher speeds than voltage computation. In some embodiments, simple clock-gated switches are used to produce even currents in the current summing branches. The clock-gated switches also serve to limit the time the cell currents are on, thereby reducing static power consumption.