On-Chip Buffer Partitioning for Overlapped Neural Network Data Transfer
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Solution Overview
Problem
Modern GPUs lack optimization for handling complex machine learning algorithms used in machine perception technology, leading to inefficiencies in sensor signal processing and computation tasks in autonomous robotics and vehicles.
Innovation Solution
An integrated circuit architecture with an on-chip data buffer and array of processing cores, configured to partition memory based on neural network computational graphs, enabling parallel execution of memory transfers and computations, and utilizing a dispatcher to manage data flow and minimize latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional GPU architectures are used for machine learning computations, then general-purpose computing capability is maintained, but processing efficiency for complex neural network algorithms deteriorates
Solution Approach 1:
The system segments the monolithic GPU architecture into specialized components: an array of processing cores optimized for neural network operations, an on-chip data buffer for high-speed intermediate storage, and an off-chip main memory for large-scale data retention. This segmentation allows each component to be optimized for its specific function, thereby improving overall processing efficiency for machine learning workloads while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent applies local quality by creating distinct memory regions within the on-chip data buffer tailored to specific computational needs. Different regions are optimized for different types of data access patterns required by neural network operations, allowing each region to have specialized characteristics that improve local processing efficiency without requiring complete architectural redesign.
2Speed
If memory is partitioned based on neural network computational graphs, then data access efficiency for specific computations is improved, but memory management complexity increases
Solution Approach 1:
The system performs preliminary action by pre-partitioning the on-chip data buffer into specific regions before neural network computations begin. Memory regions are allocated and configured based on the known structure of the neural network computational graph, allowing data to be staged in optimal locations ahead of time. This eliminates the need for complex dynamic memory management during computation, as the partitioning structure is established in advance.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting memory allocation parameters based on the specific neural network being executed. The memory partitioning scheme can be reconfigured for different computational graphs, changing parameters such as region sizes, access patterns, and storage formats to match the requirements of each specific neural network architecture, thereby optimizing data access speed without requiring permanent complex hardware structures.
3Productivity
If parallel execution of computations and data transfers is implemented, then overall processing throughput is improved, but coordination overhead between operations increases
Solution Approach 1:
The system merges the computation execution and data transfer operations into a unified parallel execution framework. Processing cores perform computations while simultaneously, other components handle data transfer between memory regions and buffers. This merging of operations into a coordinated parallel system increases throughput by eliminating sequential bottlenecks, while the unified framework manages coordination overhead through integrated control logic rather than separate management systems.
Solution Approach 2:
The patent implements continuity of useful action by ensuring that while some processing cores are executing computations, other components are continuously transferring data in parallel. The system maintains continuous productive activity across all components by overlapping computation and data transfer operations, ensuring no idle time for any component. This continuous parallel operation maximizes throughput while the coordination overhead is amortized across the continuous flow of operations.
Data Source
AI summary
Systems and methods for implementing accelerated memory transfers in an integrated circuit includes configuring a region of memory of an on-chip data buffer based on a neural network computation graph, wherein configuring the region of memory includes: partitioning the region of memory of the on-chip data buffer to include a first distinct sub-region of memory and a second distinct sub-region of memory; initializing a plurality of distinct memory transfer operations from the off-chip main memory to the on-chip data buffer; executing a first set of memory transfer operations that includes writing a first set of computational components to the first distinct sub-region of memory, and while executing, using the integrated circuit, a leading computation based on the first set of computational components, executing a second set of memory transfer operations to the second distinct sub-region of memory for an impending computation.