Parallel Signal Generation Neural Network Processing
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Solution Overview
Problem
Existing neural networks, such as WaveNets, face significant computational challenges and memory bandwidth issues due to high-throughput requirements in audio generation, making it difficult to achieve efficient parallel processing, especially when the number of layers does not conveniently match the number of processing units in hardware like GPUs.
Innovation Solution
The method involves interleaving rows of weight matrices across multiple thread blocks in parallel processing devices, allowing for parallel execution of matrix operations, and synchronizing results to facilitate efficient computation, thereby optimizing the utilization of processing units and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of layers in the neural network is increased to match the number of processing units in parallel processing hardware, then the utilization of processing units is improved, but the device complexity and difficulty of implementation increase significantly
Solution Approach 1:
The patent segments the weight matrices into multiple blocks that can be processed independently by different processing units. Instead of requiring a direct one-to-one mapping between network layers and processing units, the weight matrices are divided into manageable segments that can be distributed across available hardware resources, enabling flexible utilization without redesigning the entire network architecture.
Solution Approach 2:
The patent introduces a new dimension of parallelization by processing multiple weight matrix blocks simultaneously across different processing units. This approach moves beyond the traditional layer-by-layer sequential processing to a multi-dimensional parallel execution model where multiple matrix operations occur concurrently, improving hardware utilization without increasing network complexity.
2Device complexity
If the number of layers in the neural network is reduced to simplify implementation, then the device complexity is decreased, but the utilization of processing units deteriorates
Solution Approach 1:
The weight matrices are segmented into multiple blocks that can be processed in parallel, allowing simplified network architectures to fully utilize available processing units. Each processing unit handles a specific block of the weight matrix, ensuring high utilization without requiring a complex deep network architecture.
Solution Approach 2:
The patent creates a universal processing framework that can handle weight matrices of any size and configuration. The same parallel processing mechanism works regardless of the specific network architecture, making the solution applicable to both simple and complex networks while optimizing processing unit utilization in each case.
3Measurement precision
If sequential operations are used to ensure accurate dependency handling in neural network layers, then the measurement precision is improved, but the speed of computation deteriorates
Solution Approach 1:
The computation is segmented into independent block operations that can be executed in parallel while maintaining the required sequential dependencies within each block. This segmentation allows the system to preserve computational accuracy through proper dependency handling while achieving speedup through parallel execution of independent segments.
Solution Approach 2:
The patent performs preliminary organization of weight matrices into blocks with clearly defined dependency relationships. By pre-structuring the data and computation tasks, the system can execute parallel operations with guaranteed accuracy, as the dependency structure is established beforehand and maintained throughout the parallel computation process.
4Speed
If parallel processing is implemented without proper synchronization mechanisms, then the speed of computation is improved, but the reliability of results deteriorates due to incorrect dependency handling
Solution Approach 1:
The parallel processing is organized into segmented blocks with well-defined boundaries and dependency relationships. Each block can be processed independently by a separate processing unit, and the segmentation ensures that synchronization is only required at block boundaries, maintaining reliability while enabling parallel execution.
Solution Approach 2:
The patent introduces intermediary synchronization mechanisms that coordinate between parallel processing units without creating bottlenecks. These intermediaries manage the exchange of intermediate results and ensure proper dependency handling, allowing fast parallel computation while maintaining result correctness through controlled synchronization points.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for executing a signal generation neural network on parallel processing hardware. One of the methods includes receiving weight matrices of a layer of a signal generation neural network. Rows of a first matrix for the layer are interleaved by assigning groups of rows of the first matrix to respective thread blocks of a plurality of thread blocks. A first subset of rows of the one or more other weight matrices are assigned to a first subset of the plurality of thread blocks and a second subset of rows of the one or more other weight matrices are assigned to a second subset of the plurality of thread blocks. The first matrix operation is performed substantially in parallel by the plurality of thread blocks. The other matrix operations are performed substantially in parallel by the plurality of thread blocks.