Mixed-Signal IC Compiler Transformations for Edge AI Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional digital circuitry for neural network models is large, energy-intensive, and causes latency issues when deployed in edge devices due to the need for significant memory and compute resources, making real-time inference and prediction challenging, especially in remote or edge computing environments.
Innovation Solution
Implementing a mixed-signal integrated circuit architecture that uses a global DAC to control local DACs, along with compiler-driven operational transformations such as replacing low-precision activation functions with high-precision ones and optimizing graph components, to improve computational efficiency and reduce resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional digital circuitry is used for neural network models, then computational accuracy can be maintained, but circuit area and energy consumption increase significantly
Solution Approach 1:
The patent replaces traditional digital circuitry with a mixed-signal computing architecture that uses analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) to perform computations. This substitution allows the system to maintain computational accuracy while significantly reducing circuit area by using voltage-based analog computation for certain operations, particularly matrix multiplications in neural networks.
Solution Approach 2:
The system dynamically changes precision parameters based on computational requirements. Different parts of the neural network computation use different precision levels - some operations use full digital precision while others use analog approximation. This parameter adaptation allows the system to maintain necessary accuracy while reducing overall resource consumption.
2Quantity of substance
If traditional digital circuitry with large memory is used, then neural network weights can be stored, but energy consumption increases
Solution Approach 1:
The patent extracts the weight storage function from traditional digital memory and implements it using analog voltage levels in the mixed-signal architecture. Weights are represented as voltage values that can be directly used in analog computations, eliminating the need for large digital memory arrays and the associated energy consumption for memory access and data transfer.
Solution Approach 2:
The system merges storage and computation functions by using the same analog voltage representations for both weight storage and computation. This convergence eliminates the separate memory-access-computation pipeline of traditional digital systems, reducing energy consumption by eliminating redundant data movement between storage and processing units.
3Power
If remote computing systems are used for neural network inference, then computational power can be accessed, but latency increases
Solution Approach 1:
The patent segments the neural network computation into local and remote portions. The mixed-signal processor handles time-critical inference operations locally with low latency, while non-critical functions such as model updates and data preprocessing can be performed remotely. This segmentation allows the system to maintain low latency for critical operations while still accessing remote computational resources when needed.
Data Source
AI summary
Systems and methods for improving a computational performance of a mixed-signal integrated circuit includes identifying a suboptimal graph component of a computation graph of a subject application, wherein: (i) the computation graph comprises a plurality of graphical nodes representing computational operations and a plurality of graphical edges representing data dependencies between the graphical nodes, and (ii) the suboptimal graph component comprises a subset of the plurality of graphical nodes and the plurality of graphical edges that do not satisfy an optimal operation threshold; at compile time, selectively applying an optimizing transformation to the suboptimal graph component based on attributes of a first activation function within the suboptimal graph component, wherein the optimization transformation, when applied, transforms the suboptimal graph component to an optimal graph component that satisfies the optimal operation threshold; and reconstructing the computation graph using the optimal graph component in a place of the suboptimal graph component.