LSTM Block Apparatus with Slave and Master Modules
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Solution Overview
Problem
Existing methods for supporting Long Short-Term Memory (LSTM) blocks in Recurrent Neural Networks (RNNs) using general-purpose processors or graphics processing units (GPUs face performance bottlenecks due to low operational performance, high power consumption, and inefficient decoding processes, especially when handling multiple layer artificial neural network operations.
Innovation Solution
A specialized apparatus and method for LSTM blocks in RNNs, comprising slave computation modules and a master computation module connected via an interconnection unit, which calculate and combine partial sums to generate input gate and cell output values, and gradients, optimizing the forward and backward pass processes to enhance performance and reduce power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If general-purpose processors are used to execute RNN and LSTM operations, then flexibility and adaptability are maintained, but operational performance is low and cannot meet performance requirements
Solution Approach 1:
The processor is segmented into multiple specialized functional units including vector processing units for parallel computation, matrix processing units for neural network operations, and dedicated LSTM processing units. This segmentation enables high-performance RNN and LSTM operations while maintaining architectural flexibility through modular design.
Solution Approach 2:
The processor employs dynamic scheduling mechanisms that can adaptively allocate resources between different computational tasks. The control unit dynamically manages instruction streams to multiple functional units, enabling the system to optimize performance for specific workloads while maintaining general-purpose capability.
2Productivity
If multiple general-purpose processors are executed in parallel to improve performance, then computational capacity increases, but communication between processors becomes a performance bottleneck
Solution Approach 1:
Multiple processing units are merged into a single integrated processor architecture with shared memory hierarchies and unified control logic. This consolidation eliminates inter-processor communication bottlenecks while maintaining parallel computational capacity through internal parallelism in functional units.
Solution Approach 2:
A unified control unit acts as an intermediary that manages instruction distribution and data flow between computational units. This central coordination mechanism eliminates the need for complex inter-processor communication protocols while enabling efficient parallel execution.
3Ease of operation
If general-purpose processors decode reverse operations of RNN and LSTM into long instruction sequences, then operations can be executed, but power consumption increases significantly
Solution Approach 1:
The instruction decoding function is extracted and specialized for neural network operations. Dedicated control logic directly generates control signals for functional units without requiring full general-purpose instruction decoding, significantly reducing power consumption for RNN and LSTM operations.
Solution Approach 2:
The processor architecture is self-adapting to neural network workloads through hardware features that automatically optimize execution. Specialized functional units and control logic directly support LSTM and RNN operations without requiring complex software compilation or instruction translation, reducing the energy overhead of operation execution.
4Adaptability or versatility
If GPUs are used for RNN algorithms without special support for neural network operations, then graphics processing capability is utilized, but large amounts of previous decoding work are required bringing additional power consumption
Solution Approach 1:
The processor implements local quality optimization by providing specialized functional units specifically designed for neural network operations within the GPU architecture. Vector processing units and matrix processing units are optimized for dense linear algebra operations, while LSTM processing units handle recurrent operations, reducing the need for general-purpose decoding.
Solution Approach 2:
The architecture changes key operational parameters by implementing hardware-level support for neural network data formats and operations. This includes specialized memory structures for weight matrices, dedicated activation function units, and optimized data flow patterns that reduce computational overhead and power consumption.
Data Source
AI summary
Aspects for Long Short-Term Memory (LSTM) blocks in a recurrent neural network (RNN) are described herein. As an example, the aspects may include one or more slave computation modules, an interconnection unit, and a master computation module collectively configured to calculate an activated input gate value, an activated forget gate value, a current cell status of the current computation period, an activated output gate value, and a forward pass result.