Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method of image processing includes receiving, from a plurality of cameras, image data comprising a plurality of image frames; and determining, using a machine learning model, a BEV representation of the image data. Determining the BEV representation includes inversely projecting a BEV grid point into an image frame point. The machine learning model is trained to associate the image frame point with a plurality of predetermined indices corresponding to the image frame. Determining the BEV representation further includes determining, using the machine learning model, a set of image features of the image frame based on the plurality of predetermined indices. The set of image features are associated with the BEV grid point. Other aspects and features are also claimed and described.

Abstract: A vision transformer architecture processes images while improving hardware utilization through selective token handling. Described architectures receives input images, generates image tokens, and processes the tokens through transformer layers without requiring a classification token. An adapter layer, trained using vector comparisons between outputs with and without classification tokens, enables this efficient processing while maintaining accuracy. The adapter layer integrates with existing model weights and can merge with downstream processing layers. This approach addresses computational inefficiencies in vision transformer models when used primarily as encoders rather than classifiers. Implementations can be utilized in vision-language models for autonomous driving, mobile applications, and data center operations where processing efficiency impacts system performance.

Abstract: Systems and techniques for attention calculation optimization are described. An attention calculation system identifies tensor dimensions based on a characteristic of a tensor multiplication engine. In some examples, the tensor dimensions are matrix dimensions, for instance if the characteristic indicates that the tensor multiplication engine is optimized for matrix multiplication. The attention calculation system groups at least a subset of query data into at least one query tensor having the tensor dimensions. The attention calculation system groups at least a subset of key data into at least one key tensor having the tensor dimensions. The attention calculation system determines, using the tensor multiplication engine, a tensor multiplication including the at least one query tensor and the at least one key tensor to generate output data.

Abstract: The present disclosure provides genetically engineered biocatalysts which enable intracellular assimilation of cellodextrin. The genetically engineered biocatalyst co-expresses a cellodextrin phosphorylase (CDP) gene and a cellobiose phosphorylase (CBP) gene. Further, the genetically engineered biocatalyst includes a first synthetic promoter used to express the cellodextrin phosphorylase (CDP) gene and a second synthetic promoter used to express the cellobiose phosphorylase (CBP) gene. Furthermore, the genetically engineered biocatalyst expresses one or more cellodextrin permeases. The intracellular assimilation includes hydrolysis and phosphorolysis mechanism. Further provided are methods of using the genetically engineered biocatalysts to generate various useful end-products including alcohol and lactic acid.