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Salient Store: Enabling Smart Storage for Continuous Learning Edge Servers

Cyan Subhra Mishra, Deeksha Chaudhary, Jack Sampson, Mahmut Taylan Knademir, Chita Das

TL;DR

This paper provides a comprehensive overview of the potential of CSDs to revolutionize storage, making them not just data repositories but active participants in the computational process, and proposes a framework that aligns more closely with the growing data demands.

Abstract

As continuous learning based video analytics continue to evolve, the role of efficient edge servers in efficiently managing vast and dynamic datasets is becoming increasingly crucial. Unlike their compute architecture, storage and archival system for these edge servers has often been under-emphasized. This is unfortunate as they contribute significantly to the data management and data movement, especially in a emerging complute landscape where date storage and data protection has become one of the key concerns. To mitigate this, we propose Salient Store that specifically focuses on the integration of Computational Storage Devices (CSDs) into edge servers to enhance data processing and management, particularly in continuous learning scenarios, prevalent in fields such as autonomous driving and urban mobility. Our research, gos beyond the compute domain, and identifies the gaps in current storage system designs. We proposes a framework that aligns more closely with the growing data demands. We present a detailed analysis of data movement challenges within the archival workflows and demonstrate how the strategic integration of CSDs can significantly optimize data compression, encryption, as well as other data management tasks, to improve overall system performance. By leveraging the parallel processing capabilities of FPGAs and the high internal bandwidth of SSDs, Salient Store reduces the communication latency and data volume by ~6.2x and ~6.1x, respectively. This paper provides a comprehensive overview of the potential of CSDs to revolutionize storage, making them not just data repositories but active participants in the computational process.

Salient Store: Enabling Smart Storage for Continuous Learning Edge Servers

TL;DR

This paper provides a comprehensive overview of the potential of CSDs to revolutionize storage, making them not just data repositories but active participants in the computational process, and proposes a framework that aligns more closely with the growing data demands.

Abstract

As continuous learning based video analytics continue to evolve, the role of efficient edge servers in efficiently managing vast and dynamic datasets is becoming increasingly crucial. Unlike their compute architecture, storage and archival system for these edge servers has often been under-emphasized. This is unfortunate as they contribute significantly to the data management and data movement, especially in a emerging complute landscape where date storage and data protection has become one of the key concerns. To mitigate this, we propose Salient Store that specifically focuses on the integration of Computational Storage Devices (CSDs) into edge servers to enhance data processing and management, particularly in continuous learning scenarios, prevalent in fields such as autonomous driving and urban mobility. Our research, gos beyond the compute domain, and identifies the gaps in current storage system designs. We proposes a framework that aligns more closely with the growing data demands. We present a detailed analysis of data movement challenges within the archival workflows and demonstrate how the strategic integration of CSDs can significantly optimize data compression, encryption, as well as other data management tasks, to improve overall system performance. By leveraging the parallel processing capabilities of FPGAs and the high internal bandwidth of SSDs, Salient Store reduces the communication latency and data volume by ~6.2x and ~6.1x, respectively. This paper provides a comprehensive overview of the potential of CSDs to revolutionize storage, making them not just data repositories but active participants in the computational process.
Paper Structure (14 sections, 3 equations, 11 figures, 2 tables, 3 algorithms)

This paper contains 14 sections, 3 equations, 11 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Storage for Continuous Learning Edge Servers
  4. The Problem: Understanding the Data Flow
  5. Why Not More Compute at Storage Stacks?
  6. More Compute in Storage? Why Not?
  7. Data Compression using Neural Codec
  8. Salient Store Storage Architecture:
  9. Optimizing Lattice-Based Cryptography
  10. Implementation and Evaluation
  11. Evaluation of Encoding, Scaling and Accuracy
  12. Evaluation of Video Data Recovery and Quality:
  13. Evaluation of Lattice Based Encryption
  14. Conclusions

Figures (11)

  • Figure 1: Data flow pipeline of continuous learning edge servers with storage and data archival pipeline. The Shown storage pipeline is the preliminary focus of Salient Store .
  • Figure 2: High-level design of the Salient Store edge server - it consists of the accelerated video analytics compute along with computational storage and classical storage drives.
  • Figure 3: Microarchitectural designs of HSPM and SDMM: Hardware modules for polynomial multiplication in LBC.
  • Figure 4: Latency analysis of Salient Store on the commercial Xilinx CSD on a workstation class machine (lower is better). Compute server indicates a software only classical storage solution without CSDs.
  • Figure 5: Performance of Salient Store on larger compute and storage nodes. This experiment to mimics a consolidated edge server catering to many video streams. Compute server indicates a software only classical storage solution without CSDs.
  • ...and 6 more figures