Abstract: Aspects of the present disclosure are directed to devices and methods for performing MAC operations using a memory array as a compute-in-memory (CIM) device that can enable higher computational throughput, higher performance and lower energy consumption compared to computation using a processor outside of a memory array. In some embodiments, an activation architecture is provided using a bit cell array arranged in rows and columns to store charges that represent a weight value in a weight matrix. A read word line (RWL) may be repurposed to provide the input activation value to bit cells within a row of bit cells, while a read-bit line (RBL) is configured to receive multiplication products from bit cells arranged in a column. Some embodiments provide multiple sub-arrays or tiles of bit cell arrays.

Abstract: A system is provided. The system includes a multiply-and-accumulate circuit and a local generator. The multiply-and-accumulate circuit is coupled to a memory array and generates a multiply-and-accumulate signal indicating a computational output of the memory array. The local generator is coupled to the memory array and generates at least one reference signal at a node in response to one of a plurality of global signals that are generated according to a number of the computational output. The local generator is further configured to generate an output signal according to the signal and a summation of the at least one reference signal at the node.

Abstract: A system is provided. The system includes a multiply-and-accumulate circuit and a local generator. The multiply-and-accumulate circuit is coupled to a memory array and generates a multiply-and-accumulate signal indicating a computational output of the memory array. The local generator is coupled to the memory array and generates at least one reference signal at a node in response to one of a plurality of global signals that are generated according to a number of the computational output. The local generator is further configured to generate an output signal according to the signal and a summation of the at least one reference signal at the node.

Abstract: A system is provided. The system includes a multiply-and-accumulate circuit and a local generator. The multiply-and-accumulate circuit is coupled to a memory array and generates a multiply-and-accumulate signal indicating a computational output of the memory array. The local generator is coupled to the memory array and generates at least one reference signal at a node in response to one of a plurality of global signals that are generated according to a number of the computational output. The local generator is further configured to generate an output signal according to the signal and a summation of the at least one reference signal at the node.

Abstract: An input-shaping method for a group-modulated input scheme in a plurality of computing-in-memory applications is configured to shape a plurality of multi-bit input signals. The input-shaping method for the group-modulated input scheme in the plurality of computing-in-memory applications includes performing an input splitting step, a threshold setting step and an input shaping step. The input splitting step includes splitting the multi-bit input signals into a plurality of input sub-groups via an input-shaping unit. The threshold setting step includes setting at least one shaping threshold via the input-shaping unit. The input shaping step includes shaping at least one of the input sub-groups according to the at least one shaping threshold via the input-shaping unit to form a plurality of shaped multi-bit input signals so as to increase a probability of a bit equal to 0 occurring in the at least one of the input sub-groups.

Abstract: A system includes a global generator and local generators. The global generator is coupled to a memory array, and is configured to generate global signals, according to a number of a computational output of the memory array. The local generators are coupled to the global generator and the memory array, and are configured to generate local signals, according to the global signals. Each one of the local generators includes a first reference circuit and a local current mirror. The first reference circuit is coupled to the global generator, and is configured to generate a first reference signal at a node, in response to a first global signal of the global signals. The local current mirror is coupled to the first reference circuit at the node, and is configured to generate the local signals, by mirroring a summation of at least one signal at the node.

Abstract: Aspects of the present disclosure are directed to devices and methods for performing MAC operations using a memory array as a compute-in-memory (CIM) device that can enable higher computational throughput, higher performance and lower energy consumption compared to computation using a processor outside of a memory array. In some embodiments, an activation architecture is provided using a bit cell array arranged in rows and columns to store charges that represent a weight value in a weight matrix. A read word line (RWL) may be repurposed to provide the input activation value to bit cells within a row of bit cells, while a read-bit line (RBL) is configured to receive multiplication products from bit cells arranged in a column. Some embodiments provide multiple sub-arrays or tiles of bit cell arrays.

Abstract: Aspects of the present disclosure are directed to devices and methods for performing MAC operations using a TCAM array as a compute-in-memory (CIM) device that can enable higher computational throughput, higher performance and lower energy consumption compared to computation using a processor outside of a memory array. In some embodiments, weights in a weight matrix may be programmed in SRAMs of a TCAM bit cell array. Each SRAM may operate as a multiplier that performs a multiplication between the stored weight to an input activation value applied at a search line in the TCAM bit cell array. The two SRAMs within a TCAM bit cell may operate independently to receive independently two input activation values on their respective select lines, and to perform a multiplication operation with the stored weight in each respective SRAM.

Abstract: An input-shaping method for a group-modulated input scheme in a plurality of computing-in-memory applications is configured to shape a plurality of multi-bit input signals. The input-shaping method for the group-modulated input scheme in the plurality of computing-in-memory applications includes performing an input splitting step, a threshold setting step and an input shaping step. The input splitting step includes splitting the multi-bit input signals into a plurality of input sub-groups via an input-shaping unit. The threshold setting step includes setting at least one shaping threshold via the input-shaping unit. The input shaping step includes shaping at least one of the input sub-groups according to the at least one shaping threshold via the input-shaping unit to form a plurality of shaped multi-bit input signals so as to increase a probability of a bit equal to 0 occurring in the at least one of the input sub-groups.

Abstract: A system includes a global generator and local generators. The global generator is coupled to a memory array, and is configured to generate global signals, according to a number of a computational output of the memory array. The local generators are coupled to the global generator and the memory array, and are configured to generate local signals, according to the global signals. Each one of the local generators includes a first reference circuit and a local current mirror. The first reference circuit is coupled to the global generator, and is configured to generate a first reference signal at a node, in response to a first global signal of the global signals. The local current mirror is coupled to the first reference circuit at the node, and is configured to generate the local signals, by mirroring a summation of at least one signal at the node.

Abstract: A memory unit with an asymmetric group-modulated input scheme and a current-to-voltage signal stacking scheme for a plurality of non-volatile computing-in-memory applications is configured to compute a plurality of multi-bit input signals and a plurality of weights. A controller splits the multi-bit input signals into a plurality of input sub-groups and generates a plurality of switching signals according to the input sub-groups, and the input sub-groups are sequentially inputted to the word lines. The current-to-voltage signal stacking converter converts the bit-line current from a plurality of non-volatile memory cells into a plurality of converted voltages according to the input sub-groups and the switching signals, and the current-to-voltage signal stacking converter stacks the converted voltages to form an output voltage. The output voltage is corresponding to a sum of a plurality of multiplication values which are equal to the multi-bit input signals multiplied by the weights.

Abstract: A memory unit with an asymmetric group-modulated input scheme and a current-to-voltage signal stacking scheme for a plurality of non-volatile computing-in-memory applications is configured to compute a plurality of multi-bit input signals and a plurality of weights. A controller splits the multi-bit input signals into a plurality of input sub-groups and generates a plurality of switching signals according to the input sub-groups, and the input sub-groups are sequentially inputted to the word lines. The current-to-voltage signal stacking converter converts the bit-line current from a plurality of non-volatile memory cells into a plurality of converted voltages according to the input sub-groups and the switching signals, and the current-to-voltage signal stacking converter stacks the converted voltages to form an output voltage. The output voltage is corresponding to a sum of a plurality of multiplication values which are equal to the multi-bit input signals multiplied by the weights.

Abstract: A memory unit with multiple word lines for a plurality of non-volatile computing-in-memory applications is configured to compute a plurality of input signals and a plurality of weights. The memory unit includes a non-volatile memory cell array, a replica non-volatile memory cell array and a multi-row current calibration circuit. The non-volatile memory cell array is configured to generate a bit-line current. The replica non-volatile memory cell array includes a plurality of replica non-volatile memory cells and is configured to generate a calibration current. Each of the replica non-volatile memory cells is in the high resistance state. The multi-row current calibration circuit is electrically connected to the non-volatile memory cell array and the replica non-volatile memory cell array. The multi-row current calibration circuit is configured to subtract the calibration current from a dataline current to generate a calibrated dataline current. The dataline current is equal to the bit-line current.

Abstract: A memory unit is controlled by a word line, a reference voltage and a bit-line clamping voltage. A non-volatile memory cell is controlled by the word line and stores a weight. A clamping module is electrically connected to the non-volatile memory cell via a bit line and controlled by the reference voltage and the bit-line clamping voltage. A clamping transistor of the clamping module is controlled by the bit-line clamping voltage to adjust a bit-line current. A cell detector of the clamping module is configured to detect the bit-line current to generate a comparison output according to the reference voltage. A clamping control circuit of the clamping module switches the clamping transistor according to the comparison output and the bit-line clamping voltage. When the clamping transistor is turned on by the clamping control circuit, the bit-line current is corresponding to the bit-line clamping voltage multiplied by the weight.