Abstract: A programmable switch includes an input arbiter to analyze packet headers of incoming packets and determine which of the incoming packets are part of gradient vectors received from worker computing devices that are performing reinforcement learning. The programmable switch also includes an accelerator coupled to the input arbiter, the accelerator to: receive the incoming packets from the input arbiter; asynchronously aggregate gradient values of the incoming packets, as the gradient values are received, to generate an aggregated data packet associated with a gradient segment of the gradient vectors; and transfer the aggregated data packet to the input arbiter to be transmitted to the worker computing devices, which are to update local weights based on the aggregated data packet.

Abstract: A segmentation method and segmentation apparatus are provided, where the segmentation method includes receiving image frames comprising a current frame and an adjacent frame to the current frame, determining a feature map to aggregate the image frames based on temporal information between the current frame and the adjacent frame, extracting a feature of a region of interest (ROI) corresponding to instances included in the current frame from the feature map, predicting a class of an object corresponding to the ROI based on the feature of the ROI, and segmenting the instances by correcting an amodal mask predicted corresponding to the class of the object based on the feature of the ROI.

Abstract: A distributed network includes a first group of computing devices. Each computing device is to be coupled to two neighbor computing devices of the first group of computing device and is to: (i) aggregate gradient values received from a first neighbor computing device with local gradient values to generate a partial aggregate of gradient values that are to train a neural network model; (ii) transfer the partial aggregate of gradient values to a second neighbor computing device; and repeat (i) and (ii) until a first aggregate of gradient values from the first group of computing devices is buffered at a first computing device of the first group of computing devices. The first computing device is to transfer the first aggregate of gradient values to a second group of computing devices of the distributed network for further aggregation.

Abstract: A programmable switch includes an input arbiter to analyze packet headers of incoming packets and determine which of the incoming packets are part of gradient vectors received from worker computing devices that are performing reinforcement learning. The programmable switch also includes an accelerator coupled to the input arbiter, the accelerator to: receive the incoming packets from the input arbiter; asynchronously aggregate gradient values of the incoming packets, as the gradient values are received, to generate an aggregated data packet associated with a gradient segment of the gradient vectors; and transfer the aggregated data packet to the input arbiter to be transmitted to the worker computing devices, which are to update local weights based on the aggregated data packet.