Dynamic Deep Learning Processor Runtime Reconfiguration
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Solution Overview
Problem
Existing deep learning processor architectures are statically customized and fail to efficiently handle dynamic variations in deep learning algorithms and applications, such as mixed sparse and dense layers and varying numerical precision, leading to inefficient performance.
Innovation Solution
A dynamically reconfigurable deep learning processor is implemented using a programmable logic device with partial reconfiguration capabilities, driven by self-monitoring and optimization systems that adjust numerical precision and architecture based on runtime data and performance metrics to match dynamic demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a statically customized deep learning processor architecture is used, then the processor can be optimized for specific algorithms, but it cannot efficiently handle dynamic variations in deep learning algorithms and applications
Solution Approach 1:
The patent implements a dynamically reconfigurable deep learning processor that can change its architecture at runtime based on the specific algorithm being executed. The processor includes reconfigurable processing elements, programmable interconnect structures, and dynamic memory allocation that allow the system to adapt its computational fabric to match the requirements of different deep learning workloads, transitioning from static to dynamic architecture.
Solution Approach 2:
The patent creates a universal deep learning processor platform that can handle multiple types of algorithms including convolutional neural networks, recurrent neural networks, and transformer models. The reconfigurable architecture provides multi-functionality by allowing the same hardware platform to be dynamically adapted to execute various algorithm types without requiring separate dedicated hardware for each algorithm.
2Productivity
If the processor architecture is fixed, then manufacturing is simpler, but performance cannot be optimized for mixed sparse and dense layers with varying numerical precision
Solution Approach 1:
The patent employs parameter changes by allowing dynamic adjustment of numerical precision (e.g., switching between 8-bit, 16-bit, and 32-bit operations), activation function selections, and layer configuration parameters at runtime. The processor can reconfigure its operational parameters to match the specific requirements of sparse vs. dense layers, optimizing processing efficiency for each layer type without requiring different hardware designs.
3Adaptability or versatility
If a programmable logic device is used to provide flexibility, then the device can adapt to various system demands, but it may not fully utilize the flexibility for static deep learning processor implementations
Solution Approach 1:
The patent implements preliminary action by pre-configuring the processor with common deep learning operation templates and dataflow patterns. Before executing a specific algorithm, the system pre-loads appropriate configuration parameters, activates relevant processing elements, and prepares memory structures in advance, allowing the programmable logic device to quickly adapt to system demands without full reconfiguration overhead.
Data Source
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AI summary
Methods and systems for dynamically reconfiguring a deep learning processor by operating the deep learning processor using a first configuration. The deep learning processor then tracking one or more parameters of a deep learning program executed using the deep learning processor in the first configuration. The deep learning processor then reconfigures the deep learning processor to a second configuration to enhance efficiency of the deep learning processor executing the deep learning program based at least in part on the one or more parameters.