Non-linear Cost Function for AI Inference Optimization
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Solution Overview
Problem
Traditional hardware, such as CPUs, is not optimized for the high-performance requirements of AI inference, leading to inefficiencies in runtime, power consumption, and system resources, necessitating the development of specialized hardware like GPUs, FPGAs, and ASICs, but these solutions often face challenges in balancing tradeoffs between runtime, power, and resource usage.
Innovation Solution
The method involves obtaining an algorithm for a computational graph, computing an initial linearized metric for performance parameters, updating it with non-linear constraints, and programming hardware devices to implement the optimized computational graph, thereby optimizing AI inference tasks in terms of power consumption, idle time, and system resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If specialized hardware like GPUs, FPGAs, or ASICs is used to accelerate AI inference, then processing speed and performance are improved, but hardware complexity and resource requirements increase
Solution Approach 1:
The patent segments the computational graph into multiple layers and processes them in sequences, dividing the complex AI inference task into manageable chunks that can be optimized independently for different hardware resources
Solution Approach 2:
The patent dynamically adjusts the linearized metric weights based on non-linear constraints and resource availability, allowing the system to adapt its optimization strategy in real-time rather than using fixed optimization parameters
2Quantity of substance
If traditional hardware like CPU is used for AI inference, then system resource usage is lower, but runtime and processing efficiency deteriorate
Solution Approach 1:
The patent changes the optimization parameters from simple linear metrics to complex non-linear multi-dimensional cost functions that incorporate runtime, power consumption, and resource usage, enabling efficient execution on resource-constrained traditional hardware
3Device complexity
If linearized metrics are used for optimization, then computational complexity is reduced, but accuracy in representing non-linear performance tradeoffs deteriorates
Solution Approach 1:
The patent introduces a linearized metric as an intermediary that approximates the non-linear cost function, making the optimization problem computationally tractable while still capturing the essential tradeoffs through carefully selected weights and constraints
Solution Approach 2:
The patent uses feedback from the non-linear constraints to iteratively refine the linearized metric weights, ensuring that the simplified metric remains accurate in representing the true performance tradeoffs
Data Source
AI summary
Systems and techniques of the present disclosure enable a compiler to optimize such tradeoffs, and further enable optimization for a specific user cost function (e.g., optimization of a complex multi-dimensional and non-linear problem). Moreover, the techniques described herein can optimize in polynomial time. Accordingly, inference tasks may be optimized (e.g., based on specific applications) in terms of power consumption, idle time, the efficiency of computation, system resources, etc. For instance, by leveraging the systems and techniques described in the present disclosure, hardware designers can balance the tradeoff between runtime, power consumption, and resource usage, which are critical factors in the efficient processing of specialized tasks.