Virtualized Data Iterators for Neural Network Memory Bottlenecks
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Solution Overview
Problem
Existing neural networks and deep neural networks face performance issues due to inefficient data processing and memory management, leading to avoidable latency and computational overhead, particularly in iterating over large datasets.
Innovation Solution
The implementation of virtualized hardware iterators in neural network environments allows for optimized data processing by specifying dimensions and configuration for iteration, enabling parallel processing and improved memory management through iterator controllers that generate instructions for neuron processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the controller iterates over large amounts of data to apply processing operations in existing neural networks, then the data processing operations can be performed, but latency increases and processing performance deteriorates
Solution Approach 1:
The patent segments the data iteration process by introducing specialized iterator hardware components (row iterator, column iterator, block iterator) that divide and conquer the data traversal task. Each iterator handles specific portions of the data matrix independently, allowing parallel processing of different data segments simultaneously, thereby reducing overall latency while maintaining high productivity
Solution Approach 2:
The patent introduces iterator controllers as intermediary components between the controller and the data processing units. These iterators act as mediators that manage the complex data iteration task, generating control signals for memory access and data flow, which reduces the burden on the main controller and enables more efficient parallel processing, thus improving productivity while reducing latency
2Device complexity
If existing neural networks perform various operations without identifying critical features, then processing can be simplified, but avoidable processing time and memory space are expended
Solution Approach 1:
The patent applies local quality by enabling different iterator components to handle different portions of data with specialized processing logic. The row iterator, column iterator, and block iterator each have optimized processing characteristics suited to their specific data traversal patterns, allowing critical features to be identified and processed efficiently in their respective local contexts without unnecessarily complicating the overall system
Solution Approach 2:
The patent implements preliminary action through the iterator controllers that pre-generate control signals and prepare data traversal paths before actual processing occurs. The iterators anticipate memory access patterns and pre-fetch or pre-organize data in optimal sequences, eliminating avoidable processing delays and reducing the time required for critical feature identification without adding significant complexity
3Productivity
If inefficient management between local and external memory components is used, then memory operations can be performed, but additional computations are required and performance is stressed
Solution Approach 1:
The patent introduces iterator controllers as intermediary components that manage memory operations between local and external memory. These controllers generate precise control signals for memory access, optimizing data flow patterns and reducing unnecessary computations. By mediating memory management, the system achieves better performance without proportionally increasing complexity
Solution Approach 2:
The patent optimizes memory management by dynamically changing parameters such as data traversal patterns, memory access sequences, and buffer allocation strategies. The iterators adapt their operation modes based on data characteristics and processing requirements, transforming memory access patterns to minimize computations while maintaining productivity
Data Source
AI summary
The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as management of data among the various memory components of the NN/DNN. Using virtualized hardware iterators, data for processing by the NN/DNN can be traversed and configured to optimize the number of operations as well as memory utilization to enhance the overall performance of a NN/DNN. Operatively, an iterator controller can generate instructions for execution by the NN/DNN representative of one more desired iterator operation types and to perform one or more iterator operations. Data can be iterated according to a selected iterator operation and communicated to one or more neuron processors of the NN/DD for processing and output to a destination memory. The iterator operations can be applied to various volumes of data (e.g., blobs) in parallel or multiple slices of the same volume.