A power storage system with excellent characteristics is provided. A power storage system with a high degree of safety is provided. A power storage system with less deterioration is provided. A storage battery with excellent characteristics is provided. The power storage system includes a neural network and a storage battery. The neural network includes an input layer, an output layer, and one or more hidden layers between the input layer and the output layer. The predetermined hidden layer is connected to the previous hidden layer or the previous input layer by a predetermined weight coefficient, and connected to the next hidden layer or the next output layer by a predetermined weight coefficient. In the storage battery, voltage and time at which the voltage is obtained are measured as one of sets of data. The sets of data measured at different times are input to the input layer and the operational condition of the storage battery is changed in accordance with a signal output from the output layer.

Provided are an electronic device and a method of manufacturing the same. The electronic device may include a first device provided on a first region of a substrate; and a second device provided on a second region of the substrate, wherein the first device may include a first domain layer including a ferroelectric domain and a first gate electrode on the first domain layer, and the second device may include a second domain layer including a ferroelectric domain and a second gate electrode on the second domain layer. The first domain layer and the second domain layer may have different characteristics from each other at a polarization change according to an electric field. At the polarization change according to the electric field, the first domain layer may have substantially a non-hysteretic behavior characteristic and the second domain layer may have a hysteretic behavior characteristic.

A synapse network device includes an array of field effect transistor (FET) devices having controllable channel resistance. Pre-neurons are coupled to the array to provide input pulses to the array on first terminals of the FET devices. Post-neurons are coupled to the array to receive outputs from the array on second terminals of the FET devices and provide feedback to the array on third terminals of the FET devices, wherein a state of the FET devices is indicated based upon signals applied to the FET devices.

Disclosed herein are methods, devices, and systems for electronic components that may be or be part of an artificial neural network. The electronic component may include a substrate that has a plurality of input electrodes and a plurality of output electrodes disposed on and/or within the substrate, where the electrodes have a separation from one another. The electronic component may also include an electrically conductive network of one or more electrically conductive polymers. The electrically conductive network may be configured to electrically crosslink the plurality of input electrodes to the plurality of output electrodes.

The present invention disclosures a memory array structure, comprising an array composed of multiple memory devices arranged in rows and columns, each of the rows is set with a row leading-out wire, and each of the columns is set with a column leading-out wire, memory devices are correspondingly positioned at intersection points of each row leading-out wire and each column leading-out wire; wherein, the first terminal of each of the memory devices is individually connected to the row leading-out wire of the same row, and the second terminal of each of the memory devices is connected to a first terminal of a switch in the same column, the second terminal of the switch is connected to the column leading-out wire of the same column; wherein, each of the rows is set with one to multiple the switches, and the first terminal of each of the switches is connected to one to all of the second terminals of the memory devices in the same column. The advantage of the present invention is that the corresponding analog currents output of input signals of different specified rows according to multiply-accumulate operation requirements of each of the columns can be obtained simultaneously, thus multiply-accumulate operations of different input signals of different scales can be performed, which greatly improves operation speed and using efficiency of the array.

A memory array arranged in multiple columns and rows. Computation circuits that each calculate a computation value from cell values in a corresponding column. A column multiplexer cycles through multiple data lines that each corresponds to a computation circuit. Cluster cycle management circuitry determines a number of multiplexer cycles based on a number of columns storing data of a compute cluster. A sensing circuit obtains the computation values from the computation circuits via the column multiplexer as the column multiplexer cycles through the data lines. The sensing circuit combines the obtained computation values over the determined number of multiplexer cycles. A first clock may initiate the multiplexer to cycle through its data lines for the determined number of multiplexer cycles, and a second clock may initiate each individual cycle. The multiplexer or additional circuitry may be utilized to modify the order in which data is written to the columns.

Systems, devices, and methods related to a deep learning accelerator and memory are described. For example, a removable media (e.g., a memory card, or a USB drive) may be configured to execute instructions with matrix operands and configured with: an interface to receive a video stream; and random access memory to buffer a portion of the video stream as an input to an artificial neural network and to store instructions executable by the deep learning accelerator and matrices of the artificial neural network. Such a removable media can be used to replace an existing removable media used in a surveillance camera to record video or images. The deep learning accelerator can execute the instructions to generate analytics of the buffer portion using the artificial neural network, enabling the surveillance camera that is upgraded via the use of the removable media to provide intelligent services based on the analytics.

A human-machine-interface system comprising: register-file-memory, configured to store input-data; a first-processing-element-slice, a second-processing-element-slice, and a controller. Each of the processing-slices comprise: a register configured to store register-data; and a processing-element configured to apply an arithmetic and logic operation on the register-data in order to provide convolution-output-data. The controller is configured to: load input-data from the register-file-memory into the first-register as the first-register-data; and load: (i) input-data from the register-file-memory, or (ii) the first-register-data from the first-register, into the second-register as the second-register-data.

A neural network circuit having a novel structure is provided. A plurality of arithmetic circuits each including a register, a memory, a multiplier circuit, and an adder circuit are provided. The memory outputs different weight data in response to switching of a context signal. The multiplier circuit outputs multiplication data of the weight data and input data held in the register. The adder circuit performs a product-sum operation by adding the obtained multiplication data to data obtained by a product-sum operation in an adder circuit of another arithmetic circuit. The obtained product-sum operation data is output to an adder circuit of another arithmetic circuit, so that product-sum operations of different weight data and input data are performed.

A semiconductor device capable of efficiently recognizing images utilizing a neural network is provided. The semiconductor device includes a shift register group, a D/A converter, and a product-sum operation circuit. The product-sum operation circuit includes an analog memory and stores a parameter of a filter. The shift register group captures image data and outputs part of the image data to the D/A converter while shifting the image data. The D/A converter converts the part of the input image data into analog data and outputs the analog data to the product-sum operation circuit.