Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip placement. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip placement, comprising placing a respective macro node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the macro node to be placed at the time step to a position from the plurality of positions using the score distribution.

Abstract: Systems and methods for determining a placement for computational graph across multiple hardware devices. One of the methods includes generating a policy output using a policy neural network and using the policy output to generate a final placement that satisfies one or more constraints.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip placement. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip placement, comprising placing a respective macro node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the macro node to be placed at the time step to a position from the plurality of positions using the score distribution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing neural architecture search for machine learning models. In one aspect, a method comprises receiving training data for a machine learning, generating a plurality of candidate neural networks for performing the machine learning task, wherein each candidate neural network comprises a plurality of instances of a layer block composed of a plurality of layers, for each candidate neural network, selecting a respective type for each of the plurality of layers from a set of layer types that comprises, training the candidate neural network and evaluating performance scores for the trained candidate neural networks as applied to the machine learning task, and determining a final neural network for performing the machine learning task based at least on the performance scores for the candidate neural networks.

Abstract: Aspects of the disclosure are directed to automatically determining floor planning in chips, which factors in memory macro alignment. A deep reinforcement learning (RL) agent can be trained to determine optimal placements for the memory macros, where memory macro alignment can be included as a regularization cost to be added to the placement objective as a RL reward. Tradeoffs between the placement objective and alignment of macros can be controlled by a tunable alignment parameter.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip placement. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip placement, comprising placing a respective macro node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the macro node to be placed at the time step to a position from the plurality of positions using the score distribution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining architectures of hardware accelerators.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for compiler optimizations using a compiler optimization network. One of the methods includes receiving an input program, wherein the input program defines a graph of operation modules, wherein each node in the graph is a respective operation module, and each edge between nodes in the graph represents one operation module receiving the output generated by another operation module. The input program is processed by a compiler optimization network comprising a graph-embedding network that is configured to encode operation features and operation dependencies of the operation modules of the input program into a graph embedding representation and a policy network that is configured to generate an optimization action for each of one or more nodes encoded in the graph embedding representation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip placement. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip placement, comprising placing a respective macro node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the macro node to be placed at the time step to a position from the plurality of positions using the score distribution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip placement. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip placement, comprising placing a respective macro node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the macro node to be placed at the time step to a position from the plurality of positions using the score distribution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip placement. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip placement, comprising placing a respective macro node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the macro node to be placed at the time step to a position from the plurality of positions using the score distribution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip placement. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip placement, comprising placing a respective macro node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the macro node to be placed at the time step to a position from the plurality of positions using the score distribution.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip placement. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip placement, comprising placing a respective macro node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the macro node to be placed at the time step to a position from the plurality of positions using the score distribution.

Abstract: An inner collar is co-axially aligned with a longitudinal axis of an outer collar. The outer and inner collars are and separated by a gap. A plurality of ribs disposed within the gap and interconnect the inner collar to the outer collar. The inner collar defining an interior passage. A channel disposed within the outer collar and the inner collar. The channel extending along the longitudinal axis and opening at one side into the passage and opening at an opposite side exteriorly of the outer collar. The outer collar, inner collar, and plurality of ribs are constructed of a resiliently deformable material thereby allowing the outer and inner collars to resiliently deform for removably receiving a tubular container grip and allowing the outer collar and the plurality of ribs to resiliently deform for increasing gripping capabilities of a human hand.

Abstract: A bucket has a generally cylindrical configuration with an open top, a closed bottom, and a side wall extending there between. The bucket has an axis. The side wall has a first major section extending between the open top and the closed bottom. The side wall has a second major section extending between the open top and the closed bottom. The first and second major sections are separated to circumferentially create a first opening and a second opening. The first and second major sections are spaced from the axis by primary distances. The side wall has a first minor section and a second minor section. The first and second minor sections are located adjacent to the first and second openings respectively. The first and second minor sections are spaced from the axis by secondary distances greater than the primary distances.

Abstract: A method includes performing one or more operations as requested by a thread executing on a processor, the thread having a thread context; receiving a park request from the thread, the park request received following a request from the thread for a low latency resource, wherein the cache response time is less than or equal to a resource response threshold so as to allow the thread context to be stored and retrieved from the cache in less time than the portion of time it takes to complete the request for the low latency resource; storing the thread context in the cache; detecting that the resume condition has occurred; retrieving the thread context from the cache; and resuming execution of the thread.

Abstract: A method includes performing one or more operations as requested by a thread executing on a processor, the thread having a thread context; receiving a park request from the thread, the park request received following a request from the thread for a low latency resource, wherein the cache response time is less than or equal to a resource response threshold so as to allow the thread context to be stored and retrieved from the cache in less time than the portion of time it takes to complete the request for the low latency resource; storing the thread context in the cache; detecting that the resume condition has occurred; retrieving the thread context from the cache; and resuming execution of the thread.

Abstract: Methods and systems are provided for managing thread execution in a processor. Multiple instructions are fetched from fetch queues. The instructions satisfy the condition that they involve fewer bits than the integer processing pathway that is used to execute them. The instructions are decoded, and divided into groups. The instructions are processed simultaneously through the pathway, such that part of the pathway is used to execute one group of instructions and another part of the pathway is used to execute another group of instructions. These parts are isolated from one another so the execution of the instructions can share the pathway and execute simultaneously and independently.

Abstract: A system includes a bus, a processor operably coupled to the bus, a memory operably coupled to the bus, a plurality of input/output (I/O) devices operably coupled to the bus, where each of the I/O devices has a set of control registers, and a first shared I/O unit operably coupled to the bus. The first shared I/O unit has a plurality of shared functions and is configured to perform the shared functions, where the shared I/O functions are not included as functions on the I/O devices and the I/O devices and the processor interact with the first shared I/O unit to use one or more of the shared functions performed by the first shared I/O unit.

Abstract: Methods and systems are provided for managing thread execution in a processor. Multiple instructions are fetched from fetch queues. The instructions satisfy the condition that they involve fewer bits than the integer processing pathway that is used to execute them. The instructions are decoded, and divided into groups. The instructions are processed simultaneously through the pathway, such that part of the pathway is used to execute one group of instructions and another part of the pathway is used to execute another group of instructions. These parts are isolated from one another so the execution of the instructions can share the pathway and execute simultaneously and independently.