Abstract: Systems, apparatuses and methods may provide for technology that determines a vocabulary based on EDA tool terminologies and/or a natural language, queries and recommends, by a plurality of virtual agents, actions based on a design state and the vocabulary, wherein the plurality of agents is to include a tool agent and a designer agent, and executes a set of modifications to the design state in accordance with a collaboration between the plurality of agents. The technology may also convert a first user query from a first format to a second format, wherein the first format is incompatible with a trained AI model of a hardware architecture and the second format is compatible with the trained AI model, generate one or more predictions from the trained AI model based on the converted first user query, and select a subset of recommendations from a set of candidate architectures based on the prediction(s).

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for designing hardware.

Abstract: An apparatus for classifying an input includes a classifier and a feature extractor. The feature extractor is configured to generate a feature vector based on the input. The feature vector is also configured to set a number of elements of the feature vector to zero to produce a sparse feature vector. The sparse feature vector has the same dimensions as the feature vector generated by the feature extractor. However, the sparse feature vector includes fewer non-zero elements than the feature vector generated by the feature extractor. The feature vector is further configured to forward the sparse feature vector to the classifier to classify the input.

Abstract: An agent in a multi-agent system is provided with a policy model that controls communication of the agent with other agents in the multi-agent system. The policy model is trained by using MARL. The policy model receives more messages from one or more other agents in the multi-agent system. The policy model generates a reward score based at least on a hidden state of the agent and the one or more messages. The reward score represents an aggregation of a value of sending the message for a task and a cost of sending the message. The policy model determines whether to send the message based on the reward score. After determining to send the message, the policy model generates the message based on the hidden state of the agent and the one or more messages and sends the message to one or more other agents in the multi-agent system.

Abstract: An apparatus is provided to use NEMO search to train GNNs that can be used for mixed-precision quantization of DNNs. For example, the apparatus generates a plurality of GNNs. The apparatus further generates a plurality of new GNNs based on the plurality of GNNs. The apparatus also generates a sequential graph for a first DNN. The first DNN includes a sequence of quantizable operations, each of which includes quantizable parameters and is represented by a different node in the sequential graph. The apparatus inputs the sequential graph into the GNNs and new GNNs and evaluates outputs of the GNNs and new GNNs based on conflicting objectives of reducing precisions of the quantizable parameters of the first DNN. The apparatus then selects a GNN from the GNNs and new GNNs based on the evaluation. The GNN is to be used for reducing precisions of quantizable parameters of a second DNN.

Abstract: Logic may quantize a primary neural network. Logic may generate, by a secondary neural network logic circuitry for a primary neural network logic circuitry, quantization parameters. The primary neural network logic circuitry may comprise a primary neural network with multiple layers trainable with an objective function. Each of the multiple layers of the primary neural network may comprise multiple tensors. The secondary neural network logic circuitry may comprise one or more secondary neural networks trainable with the objective function to output the quantization parameters to the tensors.

Abstract: Automatic multi-objective hardware optimization for processing a deep learning network is disclosed. An example of a storage medium includes instructions for obtaining client preferences for a plurality of performance indicators for processing of a deep learning workload; generating a workload representation for the deep learning workload; providing the workload representation to machine learning processing to generate a workload executable, the workload executable including hardware mapping based on the client preferences; and applying the workload executable in processing of the deep learning workload.

Abstract: A method for classifying an object includes applying multiple confidence values to multiple objects. The method also includes determining a metric based on the multiple confidence values. The method further includes determining a classification of a first object from the multiple objects based on a knowledge-graph when the metric is above a threshold.

Abstract: A method of quantizing a floating point machine learning network to obtain a fixed point machine learning network using a quantizer may include selecting at least one moment of an input distribution of the floating point machine learning network. The method may also include determining quantizer parameters for quantizing values of the floating point machine learning network based at least in part on the at least one selected moment of the input distribution of the floating point machine learning network to obtain corresponding values of the fixed point machine learning network.

Abstract: A scanning device generally produces an image having a uniform resolution throughout a target region. To improve radar scanning/lidar scanning, an efficient scan approach to enable a radar device/lidar device to adoptively perform a scan of a target region based on interested regions and/or an adjustable resolution. The apparatus may be a scanning device for scanning. The apparatus performs a first scan over a target region to obtain a plurality of first scan samples at a plurality of locations within the target region. The apparatus generates a saliency map of the target region based on signal intensities of the plurality of first scan samples. The apparatus determines a salient region within the target region based on the saliency map. The apparatus performs at least one second scan over the salient region to obtain at least one second scan sample in the salient region.

Abstract: In an embodiment, a vehicle controller determines a predicted driving performance level of a vehicle driving control entity (VDCE) while the vehicle is controlled by a different VDCE. Driving control is transitioned to the VDCE, after which an actual driving performance level of the VDCE is monitored. The vehicle controller determines whether to transition driving control away from the VDCE based on the actual driving performance level. In another embodiment, after transition driving control to a VDCE, a period of heightened scrutiny used to evaluate the actual driving performance level of the VDCE specifically after the transition.

Abstract: Logic may quantize a primary neural network. Logic may generate, by a secondary neural network logic circuitry for a primary neural network logic circuitry, quantization parameters. The primary neural network logic circuitry may comprise a primary neural network with multiple layers trainable with an objective function. Each of the multiple layers of the primary neural network may comprise multiple tensors. The secondary neural network logic circuitry may comprise one or more secondary neural networks trainable with the objective function to output the quantization parameters to the tensors.

Abstract: In general, techniques are described for maintaining occupant awareness in vehicles. A device configured to maintain occupant awareness in a vehicle comprising: a processor and a display may be configured to perform the techniques. The processor may determine a location at which an occupant is gazing, and generate, when the determined location indicates that the occupant is not focused on a direction in which the vehicle is traveling, one or more contextual images capable of assisting the occupant in maintaining awareness of a context in which the vehicle is currently operating. The display may present, based on the determined location, the one or more contextual images proximate to the determined position within the cabin of the vehicle to assist the occupant in assuming control of the vehicle when the vehicle is no longer able to autonomously control the operation of the vehicle.

Abstract: A method, a computer-readable medium, and an apparatus for compressing a neural network with an unlabeled data set are provided. The apparatus may generate a first set of consecutive layers for the neural network. The first set of consecutive layers may share inputs with a second set of consecutive layers of the neural network. The apparatus may adjust weights associated with the first set of consecutive layers based on a function the difference between a first set of output values from the first set of consecutive layers and a second set of output values from the second set of consecutive layers in response to the unlabeled data set. The apparatus may remove the second set of consecutive layers from the neural network when the function of the difference between the first set of output values and the second set of output values satisfies a threshold.

Abstract: Methods, systems, and devices are described for identifying noisy regions in a skin conductance signal. The signal is divided into a plurality of windows. Two or more features of the signal within a first window are computed. At least one of the two or more features being in a frequency domain. At least two of the features are combined to obtain at least a first metric. The first metric is compared to a corresponding threshold. The first window is identified as a noisy region of the skin conductance signal based on the comparison.

Abstract: In general, techniques are described for maintaining occupant awareness in vehicles. A device configured to maintain occupant awareness in a vehicle comprising: a processor and a display may be configured to perform the techniques. The processor may determine a location at which an occupant is gazing, and generate, when the determined location indicates that the occupant is not focused on a direction in which the vehicle is traveling, one or more contextual images capable of assisting the occupant in maintaining awareness of a context in which the vehicle is currently operating. The display may present, based on the determined location, the one or more contextual images proximate to the determined position within the cabin of the vehicle to assist the occupant in assuming control of the vehicle when the vehicle is no longer able to autonomously control the operation of the vehicle.

Abstract: Certain aspects of the present disclosure relate to a method for compressed sensing (CS). The CS is a signal processing concept wherein significantly fewer sensor measurements than that suggested by Shannon/Nyquist sampling theorem can be used to recover signals with arbitrarily fine resolution. In this disclosure, the CS framework is applied for sensor signal processing in order to support low power robust sensors and reliable communication in Body Area Networks (BANs) for healthcare and fitness applications.

Abstract: Computing a non-linear function ƒ(x) in hardware or embedded systems can be complex and resource intensive. In one or more aspects of the disclosure, a method, a computer-readable medium, and an apparatus are provided for computing a non-linear function ƒ(x) accurately and efficiently in hardware using look-up tables (LUTs) and interpolation or extrapolation. The apparatus may be a processor. The processor computes a non-linear function ƒ(x) for an input variable x, where ƒ(x)=g(y(x),z(x)). The processor determines an integer n by determining a position of a most significant bit (MSB) of an input variable x. In addition, the processor determines a value for y(x) based on a first look-up table and the determined integer n. Also, the processor determines a value for z(x) based on n and the input variable x, and based on a second look-up table. Further, the processor computes ƒ(x) based on the determined values for y(x) and z(x).

Abstract: In an embodiment, a vehicle controller determines a predicted driving performance level of a vehicle driving control entity (VDCE) while the vehicle is controlled by a different VDCE. Driving control is transitioned to the VDCE, after which an actual driving performance level of the VDCE is monitored. The vehicle controller determines whether to transition driving control away from the VDCE based on the actual driving performance level. In another embodiment, after transition driving control to a VDCE, a period of heightened scrutiny used to evaluate the actual driving performance level of the VDCE specifically after the transition.

Abstract: Computing a non-linear function ƒ(x) in hardware or embedded systems can be complex and resource intensive. In one or more aspects of the disclosure, a method, a computer-readable medium, and an apparatus are provided for computing a non-linear function ƒ(x) accurately and efficiently in hardware using look-up tables (LUTs) and interpolation or extrapolation. The apparatus may be a processor. The processor computes a non-linear function ƒ(x) for an input variable x, where ƒ(x)=g(y(x),z(x)). The processor determines an integer n by determining a position of a most significant bit (MSB) of an input variable x. In addition, the processor determines a value for y(x) based on a first look-up table and the determined integer n. Also, the processor determines a value for z(x) based on n and the input variable x, and based on a second look-up table. Further, the processor computes ƒ(x) based on the determined values for y(x) and z(x).