Hitachi, Ltd.

Renesas Technology Corp.

Hitachi Astemo Ltd.

Hitachi Automotive Systems, Ltd.

Renesas Electronics Corporation

Hitachi Ulsi Systems Co., Ltd.

Goichi Ono

Masayuki Miyazaki

Koichiro Ishibashi

Riu Hirai

Yusuke Kanno

Hiroki Yamashita

Takuya Mayuzumi

Tatsuo Nakagawa

Ryosuke Fujiwara

Ming Liu

Tadashi Kishimoto

Hiroaki Ito

Kenichi Osada

Masanao Yamaoka

Kazumasa Yanagisawa

Daisuke Ishii

Taizo Yamawaki

Takashi Oshima

Masaru Kokubo

Akira Kotabe

Yoshihiko Yasu

Tadanobu Toba

Mitsuhiko Watanabe

Masato Hayashi

Yuji Ogata

Taisuke Ueta

Kenichi Shimbo

Hidehiro Toyoda

Hisanori Matsumoto

Chihiro Yoshimura

Takumi Uezono

Yasushi Sugiyama

Ryosuke Ishida

Tomofumi Hokari

Takashi Kuraishi

Yasutaka Serizawa

Akira Kitayama

Takashi Muto

Toru Motoya

Toshinobu Shinbo

Daichi Murata

Hiroaki Kurahashi

Processing time of a neural network is shortened, and the number of operations of the neural network is reduced such that a plurality of calculators can be effectively used. A neural network reduction device () that reduces the number of operations of a neural network by an operation device () including a plurality of calculators by reducing the neural network, the neural network reduction device including: a calculator allocation unit () that sets the number of calculators allocated to calculation processing of the neural network; a number-of-operations setting unit () that sets the number of operations of a reduced neural network based on the number of allocated calculators; and a neural network reduction unit () that reduces the neural network such that the number of operations of the neural network by the operation device () is equal to the number of operations set by the number-of-operations setting unit ().

Neural network compression device

An information processing device includes a parallel deep neural network configured to input a captured image of an article to deep neural network models respectively corresponding to a plurality of articles and perform inferences about the plurality of articles in parallel using the deep neural network models, a new article determination unit configured to determine whether an article included in the image is an unlearned article based on learned model information about the articles and the image, and a new article learning unit configured to learn a deep neural network model corresponding to the article determined to be unlearned based on the image and initial model configuration information about the deep neural network model when the article included in the image is determined to be an unlearned article. The new article learning unit adds the learned deep neural network model to the deep neural network models.

Information processing device and learning recognition system

The state monitoring system includes: a 3D camera configured to acquire an image in a work area; and information processing apparatus connected to the 3D camera and including a processing unit and a storage unit. The processing unit calculates a camera setting parameter that determines an imaging condition of the 3D camera with respect to a monitoring target as a target to be monitored in the work area and stores the camera setting parameter in the storage unit, determines the imaging condition of the 3D camera by applying the camera setting parameter corresponding to the monitoring target with reference to the camera setting parameter stored in the storage unit, acquires an image of the monitoring target from the 3D camera configured to image the monitoring target in the determined imaging condition, and determines a state of the monitoring target based on the acquired image of the monitoring target.

State monitoring method and state monitoring system

An information processing device includes a DNN operation unit that executes a DNN operation by a neural network including plural layers and a weight storage unit that stores a weight used in the DNN operation, and the DNN operation unit specifies data having a value larger than a predetermined threshold as operation target data among data input to a predetermined layer of the neural network, acquires a weight corresponding to the operation target data from the weight storage unit, and executes an operation of the predetermined layer based on the operation target data and the weight acquired from the weight storage unit.

Information processing device, in-vehicle control device, and vehicle control system

An arithmetic operation device causes a convolution arithmetic unit to perform a convolution arithmetic operation between a filter and target data corresponding to a size of the filter in each of a plurality of convolution layers constituting a neural network. The arithmetic operation device includes: a bit reduction unit that reduces a bit string corresponding to a first bit number from a least significant bit of the target data and reduces a bit string corresponding to a second bit number from a least significant bit of a weight that is an element of the filter for each convolution layer; and a bit addition unit that adds a bit string corresponding to a third bit number obtained by adding the first bit number and the second bit number to a least significant bit of a convolution arithmetic operation result output from the convolution arithmetic unit by inputting the target data and the weight after being reduced by the bit reduction unit to the convolution arithmetic unit.

Arithmetic operation device and arithmetic operation method

An information processing apparatus having an input device for receiving data, an operation unit for constituting a convolutional neural network for processing data, a storage area for storing data to be used by the operation unit and an output device for outputting a result of the processing. The convolutional neural network is provided with a first intermediate layer for performing a first processing including a first inner product operation and a second intermediate layer for performing a second processing including a second inner product operation, and is configured so that the bit width of first filter data for the first inner product operation and the bit width of second filter data for the second inner product operation are different from each other.

Information processing apparatus, image recognition apparatus, and parameter setting method for convolutional neural network

A computing system includes a server, and an on-vehicle device mounted on a vehicle. The server includes: a server storage part containing a learned model; a simplification part which generates a contraction information for determination of calculation precision by using the learned model and an object of inference; and a server communication part for transmitting the contraction information to the on-vehicle device. The on-vehicle device includes: a signal input part to which an output signal from a sensor mounted on the vehicle is inputted; an on-vehicle communication part for receiving the contraction information; an inference part for making an inference on the output signal and a reconfigurable logic circuit; and a reconfiguration part for configuring the inference part in the logic circuit based on the contraction information.

Computing system, server and on-vehicle device

To improve learning accuracy, while avoiding transferring of a dataset from an edge terminal to a cloud server. A management device accessible to a target object to be managed has a processor executing a program, a storage device storing the program, and a communication interface communicable with the target object. The processor executes a reception process for receiving first environmental information representing a first environment of the target object, a first generation process for generating relevant information representing relevancy between the first environmental information and second environmental information representing a second environment of the target object, a second generation process for generating a first learned model for inference by the target object in the first environment based on the relevant information and a second learned model for inference by the target object in the second environment, and a transmission process for transmitting the first learned model to the target object.

Management device, management method, and management program

Learning data of a usage environment can be efficiently collected. A recognition apparatus includes: a first neural network configured to receive input of data; a second neural network configured to receive input of the data, the second neural network having a different structure from a structure of the first neural network; a comparison unit configured to compare a first output result of the first neural network and a second output result of the second neural network; and a communication unit configured to wirelessly transmit the data to a host system configured to learn the data when a comparison result between the first output result and the second output result is different by a predetermined standard or more.

Recognition apparatus and learning system using neural networks

To achieve, in a load drive device including an H-bridge circuit, miniaturization of a capacitor or/and the constituent elements of the H-bridge circuit (e.g., reduction in volume) with circuit elements, for example, switching elements included in the H-bridge circuit, being inhibited from breaking down or destroying even in a case where a load is overloaded. The invention is disclosed in which, as a solution to the achievement, first and second modes are provided as a switching mode for the switching elementsandand switching is appropriately performed between the first and second modes.

Load drive device

Downsizing of an inductor is achieved while reducing radiation noise of an inductor current. A boosting power supply unit is, for example, provided in an ECU mounted in a vehicle or the like. The boosting power supply unit has a boosting coil, a switching element and a current control unit. The current control unitcontrols the switching elementto turn on and off by using a clock signal CKwith a higher frequency than a pulse signal SPL in a clamping period when the inductor current flowing in the boosting coil is clamped at a preset peak setting current, and controls the switching element to turn on and off by using the pulse signal SPL in periods other than the clamping period. By controlling the switching element to turn on and off in this manner, the inductor current flowing in the boosting coil is controlled.

Boost regulator with reduced inductor current noise

A semiconductor device capable of easily and properly detecting a defective element unit(s) and a quality management method for the semiconductor device are suggested. A semiconducting device simulating interactions between nodes in an interaction model is equipped with a quality management unit for managing the quality of each element unit provided corresponding to each node, wherein the quality management unit executes a specified quality test of each element unit, compares test results of the quality test with pre-given results to be obtained from the quality test, and detects a defective memory cell(s) and a defective element unit(s) based on the comparison results.

Semiconductor device and its quality management method

A semiconductor integrated circuit device that detects an operation error of an SRAM caused by a device variation fluctuating with time is provided. In the SRAM, a memory cell has a transfer MOS transistor whose gate is connected to a word line. At the time of a write test of the memory cell, a control circuit including a test/normal operation selection circuit and a word line driver circuit applies a third voltage to the word line in a preparation period before writing test data, thereafter a first voltage to the word line, and a second voltage to the word line at the end of writing. Due to this, the threshold voltage of the transfer MOS transistor, which fluctuates with time, can be controlled. Therefore, it is possible to enhance detection efficiency for a malfunctioning cell of the SRAM due to a temporal variation.

Semiconductor integrated circuit device

A transmitter circuit in which a driver circuit includes MOS transistors for bias voltage application, in which a driving current flows, cascode-connected to MOS transistors for differential signal input controlled by a voltage value of transmitted data signals, controlled by a voltage value of a bias voltage, and driver circuits include MOS transistors for bias voltage application, in which a driving current flows, cascode-connected to MOS transistors for differential signal input that is controlled by a voltage value of signals obtained by the transmitted data signals, connected to a load portion, and controlled by a voltage value of a bias voltage.

Transmitter circuit

With the objective of enhancing receiving performance of a receiver with respect to pulse signals spread by spread codes, the receiver comprises an RF front-end section which performs amplification, an AD converter section which AD-converts signals outputted from the RF front-end section, a baseband section which inversely spreads the output of the AD converter section and performs signal detection and demodulation thereon, a reception environment measuring section which measures reception environment using the input signals of the baseband section, and a parameter setting section which sets parameters for respective parts on the basis of signals outputted from the reception environment measuring section. The parameter setting section sets the parameters for the respective parts to the optimum according to the environmental condition measured by the reception environment measuring section.

Receiver

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Saitama, Japan

Goichi Ono