Abstract: Certain aspects of the present disclosure provide techniques and apparatus for processing a video stream using a machine learning model. An example method generally includes generating a first group of tokens from a first frame of the video stream and a second group of tokens from a second frame of the video stream. A first set of tokens associated with features to be reused from the first frame and a second set of tokens associated with features to be computed from the second frame are identified based on a comparison of tokens from the first group of tokens to corresponding tokens in the second group of tokens. A feature output is generated for portions of the second frame corresponding to the second set of tokens. Features associated with the first set of tokens are combined with the generated feature output into a representation of the second frame.

Abstract: The invention is notably directed to a method of in-memory processing, the aim of which is to perform matrix-vector calculations. The method relies on a device having a crossbar array structure (15). The latter includes N input lines (152) and M output lines (153), which are interconnected at cross-points defining N×M cells (155), where N?2 and M?2. The cells include respective memory systems, each designed to store K weights Wi,j,k, where K?2. Thus, the crossbar array structure includes N×M memory systems, which are capable of storing K sets of N×M weights. In order to perform multiply-accumulate (MAC) operations, the method first enables N×M active weights for the N×M cells by selecting, for each of the memory systems, a weight from its K weights and setting the selected weight as an active weight. Next, signals encoding a vector of N components are applied to the N input lines of the crossbar array structure. This causes the latter to perform MAC operations based on the vector and the N×M active weights.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for processing a video stream using a machine learning model. An example method generally includes generating a first group of tokens from a first frame of the video stream and a second group of tokens from a second frame of the video stream. A first set of tokens associated with features to be reused from the first frame and a second set of tokens associated with features to be computed from the second frame are identified based on a comparison of tokens from the first group of tokens to corresponding tokens in the second group of tokens. A feature output is generated for portions of the second frame corresponding to the second set of tokens. Features associated with the first set of tokens are combined with the generated feature output into a representation of the second frame.

Abstract: Various aspects provide methods for a computing device selecting a neural network for a hardware configuration including using an accuracy predictor to select from a search space a neural network including a first plurality of the blockwise knowledge distillation trained search blocks, in which the accuracy predictor is built using search space trained blockwise knowledge distillation search blocks. Aspects may include selecting a second plurality of the blockwise knowledge distillation trained search blocks based on criteria of predicted accuracy using the accuracy predictor for the second plurality of the blockwise knowledge distillation trained search blocks.