The present application discloses a Hamming weight calculation method performed by an operation apparatus. The operation apparatus includes a controller and a first calculator, wherein the controller sets an initial resistance state of the first memory to a low resistance state; determines a first gate voltage of the first transistor based on first bit data in a first binary sequence, and control an on-off state of the first transistor based on the first gate voltage; controls a target resistance state of the first memory based on the on-off state of the first transistor; and determines a Hamming weight of the first bit data based on a first output current on the source of the first transistor.

A monotonic counter memory system including a counter circuit and a memory circuit is provided. The counter circuit is configured to increase a count by one in response to a clock signal and output a count value of n bits, where n is a positive integer. The memory circuit includes a plurality of memory cells. The memory circuit is configured to store the count value. The stored count value changes one bit at each input count of the clock signal, and a bit switching time of the stored count value are smaller than 2.sup.n−1 times.

Examples of the present disclosure provide apparatuses and methods for determining a vector population count in a memory. An example method comprises determining, using sensing circuitry, a vector population count of a number of fixed length elements of a vector stored in a memory array.

The present disclosure discloses a full adder based on resistive-switching devices and an operation method thereof. A multi-bit full adder circuit is constituted by using a cross-bar array of resistive-switching devices, wherein data of standard sums is stored on the principle diagonal of the cross-bar array in a nonvolatile manner, and carry data is stored in adjacent units on both sides of the principle diagonal. The carry data is stored according to whether the storage loop (crosstalk loop) is turned on. With the present disclosure, the multi-bit full adder circuit is significantly simplified. Thereby, additional circuits for generating a carry signal are reduced, the circuit delay and chip area are decreased, and the adder has an ability of nonvolatile storage.

In this paper, binary stackers and counters are presented. In an embodiment, a counter uses 3-bit stacking circuits which group T bits together, followed a symmetric method to combine pairs of 3-bit stacks into 6-bit stacks. The bit stacks are then converted to binary counts, producing 6:3 and 7:3 Counter circuits with no XOR gates on the critical path. This avoids of XOR gates results in faster designs with efficient power and area utilization. In VLSI simulations, the presently-disclosed counters were 30% faster and at consumed at least 20% less power than existing parallel counters. Additionally, using the presently-disclosed counter in existing Counter Based Wallace tree multiplier architectures reduce latency and improves efficiency in term of power-delay product for 64-bit and 128-bit multipliers.

Apparatuses, methods of operating apparatuses, and corresponding computer programs are disclosed. In the apparatuses input circuitry receives input data comprising at least one data element and shift circuitry generates, for each data element of the input data, a bit-map giving a one-hot encoding representation of the data element, wherein a position of a set bit in the bit-map is dependent on the data element. Summation circuitry generates a position summation value for each position in the bit-map, wherein each position summation value is a sum across all bit-maps generated by the shift circuitry from the input data. Maximum identification circuitry determines at least one largest position summation value generated by the summation circuitry and output circuitry to generate an indication of at least one data element corresponding to the at least one largest position summation value. The statistical mode of the data elements in the input data is thereby efficiently determined.

A system for determine presence or quality of an external timing device is provided. The system may include a circuit (e.g., in a field-programmable gate array (FPGA)) having an input, an oscillator, an edge detector, a bit counter, and a calculator element. In some examples, the input may receive an input signal under test. The oscillator may advance a timer at a known rate to facilitate generation of clock samples for the input signal under test. The edge detector may measure edges of the input signal under test based on the clock samples. The circuit may include at least one bit counter to store a count associated with the measured edges for a shorter interval timer period and a longer interval timer period. The calculator element may determine presence or quality of an external timing device based on the count.

The present application discloses a Hamming weight calculation method performed by an operation apparatus. The operation apparatus includes a controller and a first calculator, wherein the controller sets an initial resistance state of the first memory to a low resistance state; determines a first gate voltage of the first transistor based on first bit data in a first binary sequence, and control an on-off state of the first transistor based on the first gate voltage; controls a target resistance state of the first memory based on the on-off state of the first transistor; and determines a Hamming weight of the first bit data based on a first output current on the source of the first transistor.

The present invention relates to a pop count-based deep learning neural network computation method, a multiply accumulator, and a device thereof. The computation method according to an exemplary embodiment of the present invention is a computation method for a deep learning neural network, including a step of generating one-hot encoding codes according to the type of first multiplication result values for a multiplication (first multiplication) of weights (W) and input values (A); a step of performing a pop-count for each generated code; and a step of accumulating result values for a constant multiplication (second multiplication) between each type of the first multiplication result value and each count value of the pop-count which are different constant values.

Examples of the present disclosure provide apparatuses and methods for determining a vector population count in a memory. An example method comprises determining, using sensing circuitry, a vector population count of a number of fixed length elements of a vector stored in a memory array.