Learnable Transform Block for Neural Network Inference Optimization
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Solution Overview
Problem
Current AI systems, such as neural networks, face high compute and memory demands, which are not effectively addressed by existing solutions that focus solely on accuracy without considering the hardware implementation costs.
Innovation Solution
Incorporating a learnable transform block before or between neural network layers to transform data into a more computational-friendly domain, reducing the size of the input and thereby minimizing the compute resources and memory usage required for processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network is trained to maintain high accuracy, then prediction accuracy is improved, but hardware implementation costs (compute resources and memory) increase
Solution Approach 1:
The transform block performs preliminary data transformation before the neural network processing, converting input data into a more computational-friendly domain. This preliminary action reduces the complexity of subsequent neural network operations, thereby lowering hardware implementation costs while maintaining prediction accuracy.
Solution Approach 2:
The transform block learns and applies parameter transformations to convert input data into a different domain that is more suitable for neural network processing. By changing the representation parameters of the data, the system reduces compute resources and memory requirements without sacrificing accuracy.
2Quantity of substance
If data is transformed using a learnable transform block, then compute resources and memory usage are reduced, but the complexity of the processing pipeline increases
Solution Approach 1:
The transform block serves as an intermediary component between the input data and the neural network. It mediates the data representation by transforming it into a more efficient domain, thereby reducing the computational burden on the neural network while adding only a single learnable layer to the processing pipeline.
Solution Approach 2:
The processing pipeline is segmented into distinct components: the transform block and the neural network. This segmentation allows the transform block to handle the computationally intensive data representation task separately, enabling the neural network to focus on pattern recognition with reduced resource requirements.
3Productivity
If the transform block learns parameters during training, then data representation efficiency is improved, but training time and computational overhead increase
Solution Approach 1:
The transform block learns data representation parameters during the training phase as a preliminary action. Once trained, this learned representation is reused during inference, significantly improving data representation efficiency while the training overhead is paid only once during the initial training period.
Solution Approach 2:
The transform block maintains continuous useful action by applying the learned transformation parameters repeatedly during inference. The initial training investment creates a reusable transformation mechanism that continuously improves processing efficiency without recurring training overhead.
Data Source
AI summary
Embodiments herein describe a learnable transform block disposed before, or in between, the neural network layers to transform received data into a more computational-friendly domain while preserving discriminative features required for the neural network to generate accurate results. In one embodiment, during a training phase, an AI system learns parameters for the transform block that are then used during the inference phase to transform received data into the computational-friendly domain that has a reduced size input. The transformed data may require less compute resources or less memory usage to process by the underlying hardware device that hosts the neural network.