Abstract

An integrated circuit formed on (i) a single semiconductor die or (ii) a plurality semiconductor dies that are integrated into a single package. The integrated circuit may include a communication interface including a serializer/deserializer (SerDes) interface; a fabric adapter communicatively coupled to the communication interface; a plurality of inference engine clusters, each inference engine cluster including a respective memory element and/or memory interface; and a data interconnect communicatively coupling each respective memory element and/or memory interfaces of the plurality of inference engine clusters to the fabric adapter. The fabric adapter may be configured to facilitate remote direct memory access (RDMA) read and write services and/or datagram communication over a cell¬ based switch fabric to and from the respective memory elements and/or memory interfaces of the plurality of inference engine clusters via the data interconnect.

IPC Classes  ?

• G06F 15/173 - Interprocessor communication using an interconnection network, e.g. matrix, shuffle, pyramid, star or snowflake

Abstract

In a system with control logic and a processing element array, two modes of operation may be provided. In the first mode of operation, the control logic may configure the system to perform matrix multiplication or 1x1 convolution. In the second mode of operation, the control logic may configure the system to perform 3x3 convolution. The processing element array may include an array of processing elements. Each of the processing elements may be configured to compute the dot product of two vectors in a single clock cycle, and further may accumulate the dot products that are sequentially computed over time.

IPC Classes  ?

• G06F 17/16 - Matrix or vector computation

Abstract

In a system with control logic and a processing element array, two modes of operation may be provided. In the first mode of operation, the control logic may configure the system to perform matrix multiplication or 1x1 convolution. In the second mode of operation, the control logic may configure the system to perform 3x3 convolution. The processing element array may include an array of processing elements. Each of the processing elements may be configured to compute the dot product of two vectors in a single clock cycle, and further may accumulate the dot products that are sequentially computed over time.

IPC Classes  ?

• G06F 15/80 - Architectures of general purpose stored program computers comprising an array of processing units with common control, e.g. single instruction multiple data processors
• G06F 9/54 - Interprogram communication

Abstract

In a system with control logic and a processing element array, two modes of operation may be provided. In the first mode of operation, the control logic may configure the system to perform matrix multiplication or 1x1 convolution. In the second mode of operation, the control logic may configure the system to perform 3x3 convolution. The processing element array may include an array of processing elements. Each of the processing elements may be configured to compute the dot product of two vectors in a single clock cycle, and further may accumulate the dot products that are sequentially computed over time.

IPC Classes  ?

• G06F 17/16 - Matrix or vector computation

Abstract

In a multiply accumulate (MAC) unit, an accumulator may be implemented in two or more stages. For example, a first accumulator may accumulate products from the multiplier of the MAC unit, and a second accumulator may periodically accumulate the running total of the first accumulator. Each time the first accumulator's running total is accumulated by the second accumulator, the first accumulator may be initialized to begin a new accumulation period. In one embodiment, the number of values accumulated by the first accumulator within an accumulation period may be a user-adjustable parameter. In one embodiment, the bit width of the input of the second accumulator may be greater than the bit width of the output of the first accumulator. In another embodiment, an adder may be shared between the first and second accumulators, and a multiplexor may switch the accumulation operations between the first and second accumulators.

IPC Classes  ?

• G06F 7/544 - Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using non-contact-making devices, e.g. tube, solid state deviceMethods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation using unspecified devices for evaluating functions by calculation
• G06F 7/509 - AddingSubtracting in bit-parallel fashion, i.e. having a different digit-handling circuit for each denomination for multiple operands, e.g. digital integrators

Abstract

A memory system comprises a plurality of memory sub-systems, each with a memory bank and other circuit components. For each of the memory sub-systems, a first buffer receives and stores a read-modify -write request (with a read address, a write address and a first operand), a second operand is read from the memory bank at the location specified by the read address, a combiner circuit combines the first operand with the second operand, an activation circuit transforms the output of the combiner circuit, and the output of the activation circuit is stored in the memory bank at the location specified by the write address. The first operand and the write address may be stored in a second buffer while the second operand is read from the memory bank. Further, the output of the activation circuit may be first stored in the first buffer before being stored in the memory bank.

IPC Classes  ?

• G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
• G06F 3/06 - Digital input from, or digital output to, record carriers

Abstract

Dynamic data quantization may be applied to minimize the power consumption of a system that implements a convolutional neural network (CNN). Under such a quantization scheme, a quantized representation of a 3x3 array of m-bit activation values may include 9 n- bit mantissa values and one exponent shared between the n-bit mantissa values (n < m); and a quantized representation of a 3x3 kernel with p-bit parameter values may include 9 q-bit mantissa values and one exponent shared between the q-bit mantissa values (q < p). Convolution of the kernel with the activation data may include computing a dot product of the 9 n-bit mantissa values with the 9 q-bit mantissa values, and summing the shared exponents. In a CNN with multiple kernels, multiple computing units (each corresponding to one of the kernels) may receive the quantized representation of the 3x3 array of m-bit activation values from the same quantization-alignment module.

IPC Classes  ?

• G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
• G06N 3/04 - Architecture, e.g. interconnection topology

Goods & Services

Semiconductors; semiconductor chips; computer hardware; recorded computer software featuring artificial intelligence for the operation of computer chips; recorded computer software featuring artificial intelligence for the autonomous driving of vehicles, autonomous navigation, autonomous control of vehicles and assisted driving; recorded computer software featuring artificial intelligence for the collection, compilation, processing, transmission and dissemination of positioning data featuring roadway, geographic, map, route planning, crowd source information, travel information enabling structuring, maintaining and using computerized models of an environment of the vehicle by processing signals from sensors, recognition of landmarks including traffic signs, road profile and lampposts and correcting ego motion estimation; interactive recorded computer software that provides roadway, navigation, geographic, map and travel information; Interactive recorded computer software featuring artificial intelligence for enabling creation or updating of computerized data models of an environment.

Goods & Services

Semiconductors; semiconductor chips; computer hardware; recorded computer software featuring artificial intelligence for the operation of computer chips; recorded computer software featuring artificial intelligence for the autonomous driving of vehicles, autonomous navigation, autonomous control of vehicles and assisted driving; recorded computer software featuring artificial intelligence for the collection, compilation, processing, transmission and dissemination of positioning data featuring roadway, geographic, map, route planning, crowd source information, travel information enabling structuring, maintaining and using computerized models of an environment of the vehicle by processing signals from sensors, recognition of landmarks including traffic signs, road profile and lampposts and correcting ego motion estimation; interactive recorded computer software that provides roadway, navigation, geographic, map and travel information; Interactive recorded computer software featuring artificial intelligence for enabling creation or updating of computerized data models of an environment.

Goods & Services

Semiconductors; semiconductor chips; computer hardware; recorded computer software featuring artificial intelligence for the operation of computer chips; recorded computer software featuring artificial intelligence for the autonomous driving of vehicles, autonomous navigation, autonomous control of vehicles and assisted driving; recorded computer software featuring artificial intelligence for the collection, compilation, processing, transmission and dissemination of positioning data featuring roadway, geographic, map, route planning, crowd source information, travel information enabling structuring, maintaining and using computerized models of an environment of the vehicle by processing signals from sensors, recognition of landmarks including traffic signs, road profile and lampposts and correcting ego motion estimation; interactive recorded computer software that provides roadway, navigation, geographic, map and travel information; Interactive recorded computer software featuring artificial intelligence for enabling creation or updating of computerized data models of an environment; all of the aforesaid goods only for use in the field of object recognition for self-driving cars and autonomous and assisted driving of vehicles.

Goods & Services

Semiconductors; semiconductor chips; computer hardware; recorded computer software featuring artificial intelligence for the operation of computer chips; recorded computer software featuring artificial intelligence for the autonomous driving of vehicles, autonomous navigation, autonomous control of vehicles and assisted driving; recorded computer software featuring artificial intelligence for the collection, compilation, processing, transmission and dissemination of positioning data featuring roadway, geographic, map, route planning, crowd source information, travel information enabling structuring, maintaining and using computerized models of an environment of the vehicle by processing signals from sensors, recognition of landmarks including traffic signs, road profile and lampposts and correcting ego motion estimation; interactive recorded computer software that provides roadway, navigation, geographic, map and travel information; Interactive recorded computer software featuring artificial intelligence for enabling creation or updating of computerized data models of an environment.

Goods & Services

Semiconductors; semiconductor chips; computer hardware; recorded computer software featuring artificial intelligence for the operation of computer chips; recorded computer software featuring artificial intelligence for the autonomous driving of vehicles, autonomous navigation, autonomous control of vehicles and assisted driving; recorded computer software featuring artificial intelligence for the collection, compilation, processing, transmission and dissemination of positioning data featuring roadway, geographic, map, route planning, crowd source information, travel information enabling structuring, maintaining and using computerized models of an environment of the vehicle by processing signals from sensors, recognition of landmarks including traffic signs, road profile and lampposts and correcting ego motion estimation; interactive recorded computer software that provides roadway, navigation, geographic, map and travel information; Interactive recorded computer software featuring artificial intelligence for enabling creation or updating of computerized data models of an environment.

Goods & Services

Semiconductors; semiconductor chips; computer hardware; recorded computer software featuring artificial intelligence for the operation of computer chips; recorded computer software featuring artificial intelligence for the autonomous driving of vehicles, autonomous navigation, autonomous control of vehicles and assisted driving; recorded computer software featuring artificial intelligence for the collection, compilation, processing, transmission and dissemination of positioning data featuring roadway, geographic, map, route planning, crowd source information, travel information enabling structuring, maintaining and using computerized models of an environment of the vehicle by processing signals from sensors, recognition of landmarks including traffic signs, road profile and lampposts and correcting ego motion estimation; interactive recorded computer software that provides roadway, navigation, geographic, map and travel information; Interactive recorded computer software featuring artificial intelligence for enabling creation or updating of computerized data models of an environment.

Abstract

FKCFFFF biases of the first layer in the memory, and classifying data received by the convolutional neural network.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 3/04 - Architecture, e.g. interconnection topology

Abstract

Systems, methods, and machine-readable media for deterministically generating labeled data for training or validating machine learning models for image analysis are described. Approaches described herein allow this training data to be generated, for example, in real time, and in response to the conditions at the location where images are generated by image sensors.

IPC Classes  ?

• G06K 9/62 - Methods or arrangements for recognition using electronic means
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/08 - Learning methods

Abstract

Systems, methods, and machine-readable media for using a convolutional neural network to generate hash strings corresponding to object instances, and thereby use the characteristic hash strings to recognize the same object instance depicted in images generated at different times and by different camera devices.

IPC Classes  ?

• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints

Abstract

Systems, methods, and machine-readable media for deterministically generating labeled data for training or validating machine learning models for image analysis, and for using such machine learning models to determine the contents of real-domain images by using a domain transfer to synthetic-appearing images are described.

IPC Classes  ?

• G06K 9/62 - Methods or arrangements for recognition using electronic means

Abstract

A hardware architecture for implementing a convolutional neural network.

IPC Classes  ?

• G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
• G06N 3/04 - Architecture, e.g. interconnection topology

Abstract

Systems, methods, and machine-readable media for determining a three-dimensional environment model of the environment of one or more camera devices, in which image processing for inferring the model may be performed at the camera devices, are described.

IPC Classes  ?

• G06T 7/579 - Depth or shape recovery from multiple images from motion
• G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments

Goods & Services

Semiconductors; semiconductor chips; computer hardware; recorded computer software featuring artificial intelligence for the operation of computer chips; recorded computer software featuring artificial intelligence for the autonomous driving of vehicles, autonomous navigation, autonomous control of vehicles and assisted driving; recorded computer software featuring artificial intelligence for the collection, compilation, processing, transmission and dissemination of positioning data featuring roadway, geographic, map, route planning, crowd source information, travel information enabling structuring, maintaining and using computerized models of an environment of the vehicle by processing signals from sensors, recognition of landmarks including traffic signs, road profile and lampposts and correcting ego motion estimation; interactive computer software that provides roadway, navigation, geographic, map and travel information; Interactive recorded computer software featuring artificial intelligence for enabling creation or updating of computerized data models of an environment.