A circuit of cells used as a memory array and capable of in-memory arithmetic is disclosed which includes a plurality of signed ternary processing, each signed ternary processing cell includes a first memory cell, adapted to hold a first digital value, a second memory cell, adapted to hold a second digital value, wherein a binary combination of the first digital value and the second digital value establishes a first signed ternary operand, a signed ternary input forming a second signed ternary operand, and a signed ternary output, wherein the signed ternary output represents a signed multiplication of the first signed ternary operand and the second signed ternary operand, a sense circuit adapted to output a subtraction result.

In some embodiments, an in-memory-computing SRAM macro based on capacitive-coupling computing (C3) (which is referred to herein as “C3SRAM”) is provided. In some embodiments, a C3SRAM macro can support array-level fully parallel computation, multi-bit outputs, and configurable multi-bit inputs. The macro can include circuits embedded in bitcells and peripherals to perform hardware acceleration for neural networks with binarized weights and activations in some embodiments. In some embodiments, the macro utilizes analog-mixed-signal capacitive-coupling computing to evaluate the main computations of binary neural networks, binary-multiply-and-accumulate operations. Without needing to access the stored weights by individual row, the macro can assert all of its rows simultaneously and form an analog voltage at the read bitline node through capacitive voltage division, in some embodiments. With one analog-to-digital converter (ADC) per column, the macro cab realize fully parallel vector-matrix multiplication in a single cycle in accordance with some embodiments.

A memory cell comprising includes a silicon-on-insulator (SOI) substrate, an electrically floating body transistor fabricated on the silicon-on-insulator (SOI) substrate, and a charge injector region. The floating body transistor is configured to have more than one stable state through an application of a bias on the charge injector region.

A memory cell comprising includes a silicon-on-insulator (SOI) substrate, an electrically floating body transistor fabricated on the silicon-on-insulator (SOI) substrate, and a charge injector region. The floating body transistor is configured to have more than one stable state through an application of a bias on the charge injector region.

In some embodiments, an in-memory-computing SRAM macro based on capacitive-coupling computing (C3) (which is referred to herein as “C3SRAM”) is provided. In some embodiments, a C3SRAM macro can support array-level fully parallel computation, multi-bit outputs, and configurable multi-bit inputs. The macro can include circuits embedded in bitcells and peripherals to perform hardware acceleration for neural networks with binarized weights and activations in some embodiments. In some embodiments, the macro utilizes analog-mixed-signal capacitive-coupling computing to evaluate the main computations of binary neural networks, binary-multiply-and-accumulate operations. Without needing to access the stored weights by individual row, the macro can assert all of its rows simultaneously and form an analog voltage at the read bitline node through capacitive voltage division, in some embodiments. With one analog-to-digital converter (ADC) per column, the macro cab realize fully parallel vector-matrix multiplication in a single cycle in accordance with some embodiments.

A memory cell comprising includes a silicon-on-insulator (SOI) substrate, an electrically floating body transistor fabricated on the silicon-on-insulator (SOI) substrate, and a charge injector region. The floating body transistor is configured to have more than one stable state through an application of a bias on the charge injector region.

The present disclosure includes apparatuses and methods related to comparing data patterns in memory. An example method can include comparing a number of data patterns stored in a memory array to a target data pattern. The method can include determining whether a data pattern of the number of data patterns matches the target data pattern without transferring data from the memory array via an input/output (I/O) line.

A ternary processing cell used as a memory cell and capable of in-memory arithmetic is disclosed which includes a first memory cell, adapted to hold a first digital value, a second memory cell, adapted to hold a second digital value, wherein a binary combination of the first digital value and the second digital value establishes a first ternary operand, a ternary input establishing a second ternary operand, and a ternary output, wherein the ternary output represents a multiplication of the first ternary operand and the second ternary operand.

A memory cell comprising includes a silicon-on-insulator (SOI) substrate, an electrically floating body transistor fabricated on the silicon-on-insulator (SOI) substrate, and a charge injector region. The floating body transistor is configured to have more than one stable state through an application of a bias on the charge injector region.

In some embodiments, an in-memory-computing SRAM macro based on capacitive-coupling computing (C3) (which is referred to herein as “C3SRAM”) is provided. In some embodiments, a C3SRAM macro can support array-level fully parallel computation, multi-bit outputs, and configurable multi-bit inputs. The macro can include circuits embedded in bitcells and peripherals to perform hardware acceleration for neural networks with binarized weights and activations in some embodiments. In some embodiments, the macro utilizes analog-mixed-signal capacitive-coupling computing to evaluate the main computations of binary neural networks, binary-multiply-and-accumulate operations. Without needing to access the stored weights by individual row, the macro can assert all of its rows simultaneously and form an analog voltage at the read bitline node through capacitive voltage division, in some embodiments. With one analog-to-digital converter (ADC) per column, the macro cab realize fully parallel vector-matrix multiplication in a single cycle in accordance with some embodiments.