Efficient FPGA Accelerator for Lightweight Convolutional Neural Networks with Balanced Dataflow

Concetti Chiave

A novel streaming architecture with hybrid computing engines and a balanced dataflow strategy is proposed to efficiently accelerate lightweight convolutional neural networks by minimizing on-chip memory overhead and off-chip memory access while enhancing computational efficiency.

Sintesi

The paper presents a novel FPGA accelerator design for efficient acceleration of lightweight convolutional neural networks (LWCNNs). The key highlights are:

1. Streaming Architecture with Hybrid Computing Engines:

   • Feature Reused Computing Engines (FRCEs) are designed for shallow layers to eliminate off-chip memory access for feature maps with a low cost of on-chip buffer size.
   • Weight Reused Computing Engines (WRCEs) are used for deep layers to significantly reduce off-chip weight traffic by maximizing weight reuse.
   • The collaboration of FRCEs and WRCEs in the streaming architecture minimizes both on-chip and off-chip memory costs.

2. Balanced Dataflow Strategy:

   • A fine-grained parallel mechanism (FGPM) is introduced to enlarge the parallel space and enable efficient resource mapping.
   • A dataflow-oriented line buffer scheme is proposed to mitigate data congestion caused by padding and large convolutional strides.
   • The balanced dataflow strategy significantly enhances the computing efficiency of the accelerator.

3. Resource-Aware Memory and Parallelism Allocation:

   • A balanced memory allocation algorithm is proposed to determine the optimal group boundary between FRCEs and WRCEs, minimizing off-chip memory access while meeting on-chip memory constraints.
   • A dynamic parallelism tuning algorithm is introduced to efficiently allocate computing resources and maximize overall throughput.

The proposed accelerator is evaluated on the Xilinx ZC706 platform using MobileNetV2 and ShuffleNetV2. It achieves an impressive performance of up to 2092.4 FPS and a state-of-the-art MAC efficiency of up to 94.58%, outperforming current LWCNN accelerators.

Statistiche

The proposed accelerator can save up to 68.3% of on-chip memory size with reduced off-chip memory access compared to the reference design.

Citazioni

"The proposed accelerator can save up to 68.3% of on-chip memory size with reduced off-chip memory access compared to the reference design."
"The proposed accelerator achieves an impressive performance of up to 2092.4 FPS and a state-of-the-art MAC efficiency of up to 94.58%, outperforming current LWCNN accelerators."

Approfondimenti chiave tratti da

A High-Throughput FPGA Accelerator for Lightweight CNNs With Balanced Dataflow

by Zhiyuan Zhao... alle arxiv.org 10-01-2024

https://arxiv.org/pdf/2407.19449.pdf

Domande più approfondite

How can the proposed accelerator architecture be extended to support other types of neural network models beyond LWCNNs?

The proposed accelerator architecture, designed specifically for lightweight convolutional neural networks (LWCNNs), can be extended to support other types of neural network models, such as standard CNNs, recurrent neural networks (RNNs), and transformers, by implementing several key modifications.

Adaptation of Computing Engines (CEs): The architecture can incorporate additional types of CEs tailored for different operations. For instance, while the current design utilizes feature map reused CEs (FRCEs) and weight reused CEs (WRCEs) for LWCNNs, standard CNNs may benefit from dedicated CEs for standard convolutions and pooling operations. RNNs could require specialized CEs for handling sequential data, while transformers would necessitate CEs optimized for attention mechanisms.

Flexible Dataflow Management: The balanced dataflow strategy can be adapted to accommodate the varying data dependencies and computational patterns of different neural network architectures. For example, transformers require handling of attention scores and multi-head attention, which could be integrated into the dataflow management system to optimize memory access and computational efficiency.

Resource Allocation Adjustments: The resource allocation algorithms can be modified to account for the unique characteristics of different neural networks. For instance, RNNs may require dynamic memory allocation strategies to handle variable-length sequences, while CNNs with larger models may need enhanced on-chip memory management to reduce off-chip access.

Scalability and Parallelism: The architecture can be designed to scale with the complexity of the neural network. This includes increasing the number of CEs and optimizing parallelism based on the specific requirements of the model being executed. For instance, deeper networks may require more parallel processing units to maintain throughput.

Integration of Mixed Precision Computing: To enhance performance across various neural network types, the architecture can support mixed precision computing, allowing for different layers to utilize varying bit-widths based on their sensitivity to precision loss. This can improve both speed and energy efficiency.

By implementing these modifications, the proposed accelerator architecture can effectively support a broader range of neural network models while maintaining high performance and efficiency.

What are the potential challenges and trade-offs in applying the balanced dataflow strategy to other CNN accelerator designs?

Applying the balanced dataflow strategy to other CNN accelerator designs presents several challenges and trade-offs that must be carefully considered:

Increased Complexity: The balanced dataflow strategy introduces additional complexity in the design and implementation of the accelerator. This complexity arises from the need to manage multiple dataflow paths and optimize resource allocation dynamically. As a result, the design may require more sophisticated control logic and scheduling mechanisms, which can complicate the overall architecture.

Resource Utilization: While the balanced dataflow strategy aims to enhance computational efficiency, it may lead to suboptimal resource utilization in certain scenarios. For instance, if the dataflow is not well-matched to the computational workload of specific layers, some processing elements (PEs) may remain underutilized, leading to inefficiencies. This is particularly relevant in architectures with fixed resource allocations.

Latency and Throughput Trade-offs: Implementing a balanced dataflow strategy may introduce latency due to the need for data reorganization and buffering. While the goal is to improve throughput, the added latency from managing data dependencies and ensuring efficient data transfer can counteract performance gains, especially in real-time applications where low latency is critical.

Memory Bandwidth Limitations: The effectiveness of the balanced dataflow strategy is heavily dependent on the available memory bandwidth. If the memory bandwidth is insufficient to support the increased data transfer demands of the optimized dataflow, it can lead to bottlenecks that negate the benefits of the strategy. This is particularly relevant for larger models that require significant off-chip memory access.

Compatibility with Existing Architectures: Integrating the balanced dataflow strategy into existing CNN accelerator designs may pose compatibility challenges. Existing architectures may have fixed dataflow patterns that do not easily accommodate the dynamic nature of the balanced dataflow approach. This could necessitate significant redesign efforts to align with the new strategy.

Performance Variability: The performance improvements achieved through the balanced dataflow strategy may vary significantly across different CNN architectures and workloads. While some models may benefit greatly from the optimized dataflow, others may see minimal improvements, leading to inconsistent performance outcomes.

In summary, while the balanced dataflow strategy offers potential benefits for CNN accelerator designs, it also introduces challenges related to complexity, resource utilization, latency, memory bandwidth, compatibility, and performance variability that must be addressed to ensure effective implementation.

How can the resource allocation algorithms be further improved to handle more complex constraints and objectives, such as power consumption or latency requirements?

To enhance resource allocation algorithms for handling more complex constraints and objectives, such as power consumption and latency requirements, several strategies can be implemented:

Multi-Objective Optimization: Resource allocation algorithms can be designed to consider multiple objectives simultaneously, such as maximizing throughput while minimizing power consumption and latency. Techniques such as Pareto optimization can be employed to find a set of optimal solutions that balance these competing objectives, allowing for more informed decision-making based on specific application requirements.

Dynamic Resource Scaling: Implementing dynamic resource scaling can help adapt the allocation of resources based on real-time workload demands. For instance, during periods of low computational demand, the algorithm can reduce the number of active processing elements (PEs) to save power, while scaling up during peak loads to meet performance targets. This approach can significantly enhance energy efficiency without compromising performance.

Power-Aware Resource Management: Incorporating power models into the resource allocation algorithms can enable more informed decisions regarding resource usage. By estimating the power consumption of different configurations, the algorithm can prioritize allocations that minimize energy usage while still meeting performance criteria. Techniques such as voltage scaling and clock gating can also be integrated to further reduce power consumption.

Latency Prediction Models: Developing accurate latency prediction models can help the resource allocation algorithm make better decisions regarding resource distribution. By analyzing the expected latency of different configurations based on historical data and workload characteristics, the algorithm can optimize resource allocation to minimize overall latency while maintaining throughput.

Feedback Mechanisms: Implementing feedback mechanisms that monitor the performance and resource utilization of the accelerator in real-time can help refine the resource allocation strategy. By continuously assessing the effectiveness of current allocations, the algorithm can adjust resource distribution dynamically to respond to changing workloads and performance requirements.

Machine Learning Approaches: Leveraging machine learning techniques can enhance the resource allocation process by enabling the algorithm to learn from past performance data and make predictions about future resource needs. Reinforcement learning, for example, can be used to optimize resource allocation strategies based on trial-and-error feedback, leading to more efficient and adaptive resource management.

Hierarchical Resource Allocation: A hierarchical approach to resource allocation can help manage complexity by breaking down the allocation process into multiple levels. For instance, high-level decisions can focus on overall resource distribution across different layers or modules, while lower-level decisions can optimize resource usage within individual layers. This can improve the granularity of resource management and enhance overall efficiency.

By implementing these strategies, resource allocation algorithms can be significantly improved to handle complex constraints and objectives, leading to more efficient and effective utilization of resources in neural network accelerators.