Cached RNN Inference for Constant-Time Low-Latency Serving
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Solution Overview
Problem
Conventional recurrent neural networks (RNNs) are challenging for low-latency applications due to their computational complexity scaling linearly with input sequence length, making them unsuitable for real-time data processing.
Innovation Solution
A system that caches the hidden state of RNNs after each data element, allowing predictions to be computed in a constant time independent of input sequence length by combining the cached hidden state with the most recent data point, and optimizes model deployment using techniques like model compression and caching to achieve near real-time inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional RNN processing is used, then accurate prediction capability is achieved, but computational complexity scales linearly with input sequence length causing high latency
Solution Approach 1:
The system pre-computes and caches hidden states at each time step during the offline phase, storing them in a cache structure before online inference. This preliminary action eliminates the need to re-process the entire sequence during online inference, reducing computational complexity from O(n) to O(1) while preserving prediction accuracy through the use of pre-computed intermediate results
Solution Approach 2:
The inference process is divided into two distinct phases: an offline phase for pre-computing and caching hidden states, and an online phase for rapid inference using the cached data. This segmentation allows complex computations to be performed in advance when time is not critical, while the online phase benefits from constant-time operations
2Loss of information
If entire sequence is processed for each inference, then complete contextual understanding is achieved, but computational resources are inefficiently utilized
Solution Approach 1:
The system performs preliminary processing by computing and caching hidden states for each time step during the offline phase. This preserves complete contextual information in the cached hidden states while enabling efficient online inference, as the contextual understanding is pre-established and stored for rapid retrieval
Solution Approach 2:
Instead of re-processing the entire input sequence for each inference request, the system creates and stores copies of hidden states at each time step in a cache structure. These cached copies contain the essential contextual information needed for inference, eliminating redundant computations while maintaining complete contextual understanding
3Measurement precision
If model capacity is increased for accurate predictions, then prediction capability is improved, but training and inference time increases
Solution Approach 1:
The system performs the computationally intensive operations of processing high-capacity models in advance during the offline phase, where time is not critical. The results are cached and stored, enabling rapid online inference even with high-capacity models. This separates the heavy computational burden from the time-sensitive inference operations
Solution Approach 2:
The system dynamically adjusts the amount of computation performed based on the phase: extensive computation with high-capacity models during the offline phase, and minimal constant-time computation during the online phase. This dynamic approach allows the use of high-capacity models for accurate predictions while meeting low-latency requirements for online inference
Data Source
AI summary
Systems, apparatuses, and methods for implementation of an inference or prediction process using a recurrent neural network (RNN) that is particularly advantageous for low-latency applications. Embodiments introduce an implementation of a recurrent neural network-based system which results in a fixed inference time (i.e., a constant computation time to perform an inference stage) that is independent of input data sequence length. Embodiments may be used to implement real-time data mapping and management and perform an inference strategy that enables the system to be used for serving different types of models, including sequential deep neural networks for low latency (i.e., real-time, or close to real-time) applications.