Enabling Fault-Tolerant RRAM-based Deep Neural Network Accelerators using Drop-Connect Training

Core Concepts

A machine learning technique that enables the deployment of defect-prone RRAM accelerators for DNN applications without modifying the hardware, retraining the neural network, or implementing additional detection circuitry/logic.

Abstract

The paper presents a thorough investigation and analysis of a drop-connect inspired technique as a fault-tolerance solution for RRAM-based DNN accelerators. The key idea involves incorporating a drop-connect approach during the training phase of a DNN, where random subsets of weights are selected to emulate fault effects, thereby equipping the DNN with the ability to learn and adapt to RRAM defects.
The results demonstrate the viability of the drop-connect approach, even in the presence of high defect rates (up to 30%), where the degradation of DNN accuracy can be as low as less than 1% compared to the fault-free version, while incurring minimal system-level runtime/energy costs.
The paper also explores various algorithm and system-level design considerations and trade-offs, such as:

The optimal drop-connect rate during training for a given fault rate
Increasing the network width to compensate for information loss due to drop-connect
Executing 1x1 convolution layers on traditional fault-free devices to improve efficiency
Modifying the structure of critical layers (e.g., increasing kernel size of shortcut layers in ResNet) to enhance drop-connect
The authors conclude that the drop-connect-inspired approach is a viable solution, especially when the fault rate is relatively low or when a modest accuracy loss is acceptable. The systematic exploration of the various trade-offs is crucial to select the best design point for a given use scenario.

Stats

Even with a 30% fault rate, the degradation in DNN accuracy can be less than 1% compared to the fault-free version.
Increasing the network width by 20%/60% can yield up to 4%/12.5% improvements in test accuracy, respectively, while incurring up to 42.6%/153.3% performance/energy costs.
Executing 1x1 convolution layers on traditional fault-free architectures is crucial to achieve high network accuracy.

Quotes

"The key idea involves incorporating a drop-connect approach during the training phase of a DNN, where random subsets of weights are selected to emulate fault effects (e.g., set to zero to mimic stuck-at-1 faults), thereby equipping the DNN with the ability to learn and adapt to RRAM defects with the corresponding fault rates."
"Our results demonstrate the viability of the drop-connect approach, coupled with various algorithm and system-level design and trade-off considerations. We show that, even in the presence of high defect rates (e.g., up to 30%), the degradation of DNN accuracy can be as low as less than 1% compared to that of the fault-free version, while incurring minimal system-level runtime/energy costs."

Key Insights Distilled From

Drop-Connect as a Fault-Tolerance Approach for RRAM-based Deep Neural Network Accelerators

by Mingyuan Xia... at arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15498.pdf

Deeper Inquiries

How can the drop-connect inspired approach be further extended or combined with other fault-tolerance techniques to achieve even higher levels of accuracy and robustness for RRAM-based DNN accelerators?

The drop-connect approach can be enhanced by integrating it with other fault-tolerance techniques to improve accuracy and robustness in RRAM-based DNN accelerators. One potential extension is to combine drop-connect with error-correcting codes (ECC) to provide additional resilience against faults. By encoding the weights or activations with ECC, the system can detect and correct errors introduced by faults in RRAM devices, thereby improving the overall reliability of the neural network.
Furthermore, incorporating redundancy through techniques like redundant computing units or redundant pathways can complement the drop-connect approach. Redundancy allows for error detection and correction by comparing outputs from redundant components, enabling the system to identify and mitigate errors caused by faulty RRAM cells. This redundancy can be strategically implemented in critical parts of the network to ensure accurate results even in the presence of faults.
Additionally, adaptive reconfiguration strategies can be employed in conjunction with drop-connect to dynamically adjust the network architecture based on the detected faults. By monitoring the performance degradation caused by faults, the system can reconfigure itself to bypass or mitigate the impact of faulty components, thereby maintaining accuracy and robustness in RRAM-based DNN accelerators.

What are the potential limitations or drawbacks of the drop-connect approach, and how can they be addressed through alternative machine learning or system-level techniques?

While the drop-connect approach offers a promising solution for fault tolerance in RRAM-based DNN accelerators, it has certain limitations that need to be addressed. One drawback is the potential loss of information and accuracy when applying drop-connect to critical layers, especially those with 1x1 convolution kernels. This information loss can lead to suboptimal performance, particularly in complex networks like ResNet20.
To mitigate this limitation, alternative machine learning techniques such as adaptive pruning or dynamic weight adjustment can be employed. These techniques allow for selective modification of weights based on their importance, ensuring that critical information is preserved even in the presence of faults. By dynamically adjusting the network parameters, the system can maintain accuracy while accommodating the effects of faulty RRAM cells.
Furthermore, system-level techniques like intelligent fault detection and recovery mechanisms can complement the drop-connect approach. By integrating real-time monitoring of RRAM device health and implementing proactive fault mitigation strategies, the system can preemptively address faults before they impact network performance. This proactive approach can enhance the overall reliability and robustness of RRAM-based DNN accelerators.

Given the insights on the importance of 1x1 convolution layers, how can the network architecture be systematically designed or modified to better leverage the strengths of both RRAM-based and traditional hardware for optimal performance and energy efficiency?

To leverage the strengths of both RRAM-based and traditional hardware for optimal performance and energy efficiency, the network architecture can be systematically designed or modified in the following ways:

Hybrid Architecture Design: Implement a hybrid architecture that strategically allocates tasks between RRAM-based accelerators and traditional processors based on their strengths. Offload computationally intensive tasks to RRAM accelerators while executing critical operations, especially those involving 1x1 convolution layers, on traditional hardware to ensure high accuracy and efficiency.

Dynamic Task Allocation: Develop dynamic task allocation algorithms that adaptively distribute computations based on the current system conditions. By monitoring performance metrics and workload demands, the system can intelligently assign tasks to the most suitable hardware platform, optimizing both performance and energy efficiency.

Selective Offloading: Selectively offload specific layers or operations to RRAM-based accelerators based on their fault tolerance requirements. Critical layers that are sensitive to faults can be executed on traditional hardware, while less critical tasks can be delegated to RRAM accelerators to capitalize on their energy efficiency and speed.

Fault-Aware Architecture: Design fault-aware architectures that incorporate fault detection and mitigation mechanisms at the system level. By integrating fault tolerance strategies with network architecture design, the system can proactively handle faults in RRAM devices, ensuring continuous operation and high accuracy.

By implementing these systematic design principles, the network architecture can effectively leverage the strengths of both RRAM-based and traditional hardware, optimizing performance, accuracy, and energy efficiency in DNN accelerators.