Efficient Sparse Processing-in-Memory Architecture (ESPIM) for Accelerating Unstructured Sparse Matrix-Vector Inference

Core Concepts

ESPIM proposes a novel sparse PIM architecture that efficiently handles the challenges of uncertainty, irregularity, and load imbalance introduced by unstructured sparsity, while staying within PIM's area and energy constraints.

Abstract

The content presents ESPIM, an efficient sparse processing-in-memory (PIM) architecture for accelerating unstructured sparse matrix-vector (MV) inference in machine learning models.
Key highlights:

To avoid the 10x increase in vector broadcast bandwidth demand due to sparsity, ESPIM employs a fine-grained interleaved layout where each vector broadcast is shared among multiple matrix rows in each bank, cutting the bandwidth demand.
ESPIM exploits the observation that the sparsity is data-dependent but static and known before inference, and proposes static data-dependent scheduling (SDDS) to derive the sparse MV's cycle-level schedule and insert the appropriate stalls for correctness, avoiding the need for complex on-chip control.
To address the latency of sequential vector slice broadcasts, ESPIM decouples the matrix cell values and indices, placing the indices ahead of the values to enable prefetching of the vector elements. ESPIM extends SDDS to handle the decoupled prefetching for performance and correctness.
ESPIM simplifies the switch required to select the vector elements that match the matrix cells, and extends SDDS to improve performance by achieving fewer conflicts in the simplified switch.

The proposed techniques allow ESPIM to achieve 2x average (up to 4.2x) speedup and 34% average (up to 63%) lower energy compared to the state-of-the-art Newton PIM architecture, while incurring under 5% area overhead.

Stats

Achieving 2x average (up to 4.2x) speedup over Newton.
Achieving 34% average (up to 63%) lower energy than Newton.
Incurring under 5% area overhead compared to Newton.

Quotes

"Emerging machine learning (ML) models (e.g., transformers) involve memory pin bandwidth-bound matrix-vector (MV) computation in inference."
"Sparsity – zeros in operands – can improve speed and energy in inference by reducing the work."
"Sparsity introduces the significant challenges of uncertainty, irregularity, and load imbalance to dense PIMs like Newton."

Key Insights Distilled From

Efficient Sparse Processing-in-Memory Architecture (ESPIM) for Machine Learning Inference

by Mingxuan He,... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04708.pdf

Deeper Inquiries

How can ESPIM's techniques be extended to handle structured sparsity patterns in addition to unstructured sparsity

To extend ESPIM's techniques to handle structured sparsity patterns, we can modify the fine-grained interleaving layout and the decoupled prefetching mechanisms. For structured sparsity, where certain patterns of zeros are known in advance, we can design specific indexing schemes that cater to these patterns. By organizing the matrix cells and indices based on the structured sparsity pattern, we can optimize the layout to efficiently handle the specific sparsity structure. Additionally, the decoupled prefetching can be adapted to prioritize fetching elements based on the known structured sparsity pattern, reducing unnecessary prefetching and improving efficiency in matching vector elements to matrix cells.

What are the potential trade-offs between the fine-grained interleaving layout and the decoupled prefetching in ESPIM, and how can they be further optimized

The potential trade-offs between the fine-grained interleaving layout and the decoupled prefetching in ESPIM lie in the balance between bandwidth utilization and latency. The fine-grained interleaving layout optimizes the reuse of vector broadcasts by grouping sparse matrix rows together, reducing the number of required broadcasts. However, this may introduce additional complexity in managing the interleaved layout. On the other hand, decoupled prefetching aims to reduce stalls by prefetching vector elements based on indices, improving efficiency but potentially increasing latency due to the prefetching process. To further optimize this, a dynamic prefetching strategy based on real-time data access patterns can be implemented to adaptively prefetch elements that are likely to be needed next, reducing latency without compromising efficiency.

Given the static nature of sparsity in ML models, how can ESPIM's techniques be leveraged to enable efficient sparse model training and adaptation on the edge

The static nature of sparsity in ML models, as leveraged by ESPIM's techniques, can be beneficial for efficient sparse model training and adaptation on the edge. By utilizing the static sparsity information known during training, ESPIM can optimize the scheduling and data dependencies for sparse models, enabling faster inference and reduced energy consumption. To enable efficient sparse model training and adaptation on the edge, ESPIM's techniques can be extended to incorporate dynamic reconfiguration capabilities based on evolving sparsity patterns. This adaptability can allow the system to adjust its operations in real-time to accommodate changes in sparsity, ensuring optimal performance for sparse model training and adaptation scenarios.