Attention-Based Neural Network Hardware Implementation
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Solution Overview
Problem
Implementing attention-based neural networks in hardware is inefficient due to the need for processing sequences of variable lengths, which are not compatible with fixed-function hardware that typically requires inputs of fixed lengths.
Innovation Solution
Paddling variable-length input sequences to a fixed length and using attention masks to ignore padding values, allowing the network to focus on relevant elements by generating self-attention and cross-attention masks through outer product operations, enabling efficient processing in hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If variable-length input sequences are processed directly, then the network maintains accuracy for different sequence lengths, but fixed-function hardware cannot process them efficiently
Solution Approach 1:
The input sequence is segmented into valid elements and padding elements. A padding mask is generated to identify which elements are actual data versus padding. This segmentation allows the hardware to process fixed-length sequences while ignoring the padding portions, thus resolving the contradiction between fixed hardware requirements and variable input lengths.
Solution Approach 2:
A padding mask is introduced as an intermediary between the fixed-length input sequence and the attention mechanism. The mask mediates by indicating which positions contain valid data and which contain padding, allowing the hardware to efficiently skip padding elements during attention calculations without requiring variable-length input support.
2Ease of manufacture
If padding values are included in attention calculations, then fixed-length processing is simplified, but padding values interfere with the attention mechanism's ability to focus on relevant elements
Solution Approach 1:
The padding mask extracts and isolates the padding elements from the valid input elements. By generating an attention mask from the padding mask (where padding positions receive -inf values and valid positions receive 0), the harmful padding values are effectively removed from attention calculations, allowing the attention mechanism to focus solely on relevant elements while maintaining fixed-length processing simplicity.
Solution Approach 2:
The padding values, which would normally harm the attention mechanism by introducing irrelevant information, are converted into a benefit through the padding mask. The mask transforms the presence of padding into useful information about which positions to ignore, enabling the system to maintain fixed-length processing while preventing padding interference through the outer product operation that generates attention masks.
3Reliability
If attention masks are generated through outer product operations, then padding values are effectively excluded from calculations, but additional computational steps are required
Solution Approach 1:
The padding mask is generated in advance during the input preparation phase, before the attention mechanism processes the data. This preliminary action of creating the mask allows the main processing pipeline to remain simple and fixed-length, while the mask generation (including the outer product operation) is performed as a separate preparatory step that ensures calculation accuracy without complicating the core processing path.
Data Source
AI summary
A computer-implemented method for selecting numerical formats suitable for use in configuring a hardware implementation of an attention-based neural network. A dataset of test input sequences for the neural network is obtained. Each test input sequence is padded with padding values. For each padded input sequence, a padding mask is generated identifying the part of the padded input sequence that contains the padding values. An attention mask is generated from each padding mask, using an outer product operation. The padded input sequences and attention masks are processed through the neural network. During the processing, statistics are collected, describing ranges of values obtained at various layers of the neural network. Numerical formats are selected for the various layers based on the collected statistics.