Efficient SRAM-PIM Architecture Design by Exploiting Unstructured Bit-Level Sparsity in Neural Networks

Основные понятия

Bit-level sparsity in neural network models can significantly boost computational efficiency, but traditional digital SRAM-PIM architectures struggle to effectively exploit this unstructured sparsity. The proposed Dyadic Block PIM (DB-PIM) framework, through an algorithm-architecture co-design approach, efficiently utilizes unstructured bit-level sparsity to achieve remarkable speedups and energy savings.

Аннотация

The paper presents a novel algorithm and architecture co-design framework, called Dyadic Block PIM (DB-PIM), to effectively exploit unstructured bit-level sparsity in neural network models for SRAM-based Processing-in-Memory (PIM) architectures.

Key highlights:

1. Bit-level sparsity in neural networks can provide significant opportunities for improving computational efficiency, but traditional digital SRAM-PIM architectures struggle to effectively leverage this unstructured sparsity due to their rigid crossbar structure.
2. The proposed FTA (Fixed Threshold Approximation) algorithm, coupled with a distinctive sparsity pattern termed "dyadic block (DB)", preserves the random distribution of non-zero bits to maintain accuracy while restricting the number of non-zero bits in each weight to improve regularity.
3. The customized DB-PIM architecture includes dyadic block multiplication units (DBMUs) and Canonical Signed Digit (CSD)-based adder trees, specifically designed for efficient MAC operations on randomly distributed non-zero bits.
4. An input pre-processing unit (IPU) further refines performance and efficiency by capitalizing on block-wise input sparsity.
5. Experimental results show that the proposed DB-PIM framework achieves a remarkable speedup of up to 7.69× and energy savings of 83.43% compared to a dense digital PIM baseline.

Статистика

The paper provides the following key statistics:

The ratio of zero bits in weights ranges from 60% to 85% across various neural network models.
The ratio of N consecutive zero bits in input features can reach up to 80% for groups of 8 bits and 70% for groups of 16 bits.

Цитаты

"Bit-level sparsity in neural network models harbors immense un-tapped potential. Eliminating redundant calculations of randomly distributed zero-bits significantly boosts computational efficiency."
"To address this challenge, we propose Dyadic Block PIM (DB-PIM), a groundbreaking algorithm-architecture co-design framework."
"Results show that our proposed co-design framework achieves a remarkable speedup of up to 7.69× and energy savings of 83.43%."

Ключевые выводы из

Towards Efficient SRAM-PIM Architecture Design by Exploiting Unstructured Bit-Level Sparsity

by Cenlin Duan,... в arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09497.pdf

Дополнительные вопросы

How can the proposed DB-PIM framework be extended to leverage both value-level and bit-level sparsity simultaneously for even greater performance and energy efficiency improvements

The DB-PIM framework can be extended to leverage both value-level and bit-level sparsity simultaneously by integrating techniques that address each type of sparsity effectively. To achieve this, a hybrid approach can be implemented where the FTA algorithm, designed for bit-level sparsity, can be combined with value-level sparsity techniques such as block-wise zero-skipping or structured pruning. By incorporating both approaches, the framework can identify and exploit zero values in weights and input features, as well as optimize the processing of non-zero bits at the bit-level. This integration would require a comprehensive algorithm-architecture co-design strategy to ensure seamless coordination between the different sparsity types. Additionally, the hardware design of the PIM macro and processing units would need to be adaptable to handle the diverse sparsity patterns efficiently. By leveraging both value-level and bit-level sparsity simultaneously, the DB-PIM framework can achieve even greater performance improvements and energy efficiency gains in neural network computations.

What are the potential challenges and trade-offs in applying the DB-PIM approach to other types of neural network architectures beyond convolutional and fully-connected layers

Applying the DB-PIM approach to neural network architectures beyond convolutional and fully-connected layers may present challenges and trade-offs due to the unique characteristics of different types of networks. For architectures like recurrent neural networks (RNNs) or transformers, which involve sequential processing and attention mechanisms, the utilization of sparsity may vary. Challenges may arise in identifying and exploiting sparsity patterns in the context of sequential data or complex attention mechanisms. Trade-offs could include the need for specialized processing units or modifications to the algorithm to accommodate the specific requirements of these architectures. Additionally, the scalability of the DB-PIM framework to larger and more complex networks may pose challenges in terms of hardware resources and computational efficiency. Adapting the DB-PIM approach to diverse neural network architectures would require thorough analysis of the sparsity characteristics and computational dependencies unique to each architecture, along with tailored solutions to address these challenges effectively.

How can the DB-PIM framework be adapted to support dynamic sparsity patterns that may change during the inference or training process of neural networks

To support dynamic sparsity patterns that may change during the inference or training process of neural networks, the DB-PIM framework can be adapted with dynamic reconfiguration capabilities. This adaptation would involve implementing mechanisms for real-time detection and adjustment of sparsity patterns based on the changing requirements of the neural network model. One approach could be to incorporate feedback loops that continuously monitor the sparsity levels in weights and input features and dynamically reconfigure the processing units accordingly. This dynamic sparsity management system would need to be integrated into the algorithm and architecture co-design framework, allowing for on-the-fly adjustments to optimize computation based on the evolving sparsity patterns. Additionally, the hardware design of the PIM macro and processing units would need to be flexible and reconfigurable to accommodate varying sparsity patterns efficiently. By enabling dynamic sparsity support, the DB-PIM framework can enhance adaptability and performance in scenarios where sparsity patterns fluctuate during neural network operations.