Neural Network Hardware Co-Design Optimization

The patent changes the parameters of both neural network architecture and hardware architecture simultaneously through a unified optimization process. By representing hardware architecture choices as differentiable parameters and combining them with neural network parameters in a single loss function, the system can adjust both sets of parameters to achieve optimal accuracy while controlling resource consumption through the differentiable hardware cost model.

The patent creates a universal optimization framework that simultaneously handles both neural network architecture search and hardware architecture selection. The unified loss function serves multiple purposes: optimizing neural network accuracy, controlling hardware cost, and ensuring differentiability. This multi-functional approach eliminates the need for separate optimization processes and enables joint optimization of software and hardware components.

The patent implements feedback through the differentiable hardware cost model that provides gradient information about latency and other resource constraints back to the architecture optimization process. The hardware cost function C(h) is designed to be differentiable with respect to hardware parameters, allowing the optimization algorithm to receive feedback on how architectural changes affect latency and adjust the design accordingly to meet timing constraints while maintaining accuracy.

The patent makes the hardware architecture dynamic and adaptable by representing it as a set of parameterizable components that can be automatically configured through the optimization process. Instead of fixed hardware designs, the system uses configurable hardware architectures where parameters such as layer configurations, operation types, and resource allocations can be dynamically adjusted based on the specific neural network requirements and resource constraints, achieving performance optimization without excessive complexity.