Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed to schedule resources for model inference. An example apparatus includes interface circuitry, machine-readable instructions, and programmable circuitry to be programmed by the machine-readable instructions to: generate first assignment data structures to respectively assign portions of artificial intelligence (AI) models to respective compute devices for execution, change the assignments in the assignment data structures to generate offspring data structures, change ones of the assignments in the offspring data structures, replace ones of the first assignment data structures with ones of the changed offspring data structures to generate second assignment data structures, and assign the portion of the AI models for execution by the compute devices based on the second assignment data structures.

Abstract: Technology providing in-memory neural network protection can include a memory to store a neural network, and a processor executing instructions to generate a neural network memory structure having a plurality of memory blocks in the memory, scatter the neural network among the plurality of memory blocks based on a randomized memory storage pattern, and reshuffle the neural network among the plurality of memory blocks based on a neural network memory access pattern. Scattering the neural network model can include dividing each layer of the neural network into a plurality of chunks, for each layer, selecting, for each chunk of the plurality of chunks, one of the plurality of memory blocks based on the randomized memory storage pattern, and storing each chunk in the respective selected memory block. The plurality of memory blocks can be organized into a groups of memory blocks and be divided between stack space and heap space.

Abstract: Systems, apparatuses and methods include technology that identifies data that is to be transferred from a first device to a second device. The technology classifies the data into a category from a plurality of categories, selects a compression scheme from a plurality of compression schemes based on the category and compresses the data based on the compression scheme.

Abstract: Devices and techniques related to implementing a distributed machine vision camera system are discussed. The distributed machine vision camera system includes distributed cameras that attain raw image data and directly apply deep learning engines to the raw image data to perform machine vision, such that the distributed cameras do not have the capability to convert the raw image data to viewable image data, and a centralized device to receive the raw image data when a positive machine vision indicator is detected and convert the raw image data to viewable image data for inspection by a user.