Sustainable AI Processing at the Edge

• URL: http://arxiv.org/abs/2207.01209v1
• Date: Mon, 4 Jul 2022 05:32:12 GMT
• Title: Sustainable AI Processing at the Edge
• Authors: S\'ebastien Ollivier, Sheng Li, Yue Tang, Chayanika Chaudhuri, Peipei Zhou, Xulong Tang, Jingtong Hu, and Alex K. Jones (University of Pittsburgh)
• Abstract summary: This paper explores tradeoffs of convolutional neural network acceleration engines for both inference and on-line training. In particular, we explore the use of processing-in-memory (PIM) approaches, mobile GPU accelerators, and recently released FPGAs.
• Score: 10.240738732324186
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Edge computing is a popular target for accelerating machine learning algorithms supporting mobile devices without requiring the communication latencies to handle them in the cloud. Edge deployments of machine learning primarily consider traditional concerns such as SWaP constraints (Size, Weight, and Power) for their installations. However, such metrics are not entirely sufficient to consider environmental impacts from computing given the significant contributions from embodied energy and carbon. In this paper we explore the tradeoffs of convolutional neural network acceleration engines for both inference and on-line training. In particular, we explore the use of processing-in-memory (PIM) approaches, mobile GPU accelerators, and recently released FPGAs, and compare them with novel Racetrack memory PIM. Replacing PIM-enabled DDR3 with Racetrack memory PIM can recover its embodied energy as quickly as 1 year. For high activity ratios, mobile GPUs can be more sustainable but have higher embodied energy to overcome compared to PIM-enabled Racetrack memory.

Related papers

• HH-PIM: Dynamic Optimization of Power and Performance with Heterogeneous-Hybrid PIM for Edge AI Devices [1.8749305679160366]
  This study introduces a Heterogeneous-Hybrid PIM (HH-PIM) architecture, comprising high-performance MRAM-SRAM PIM modules and low-power MRAM-SRAM PIM modules. We show that the proposed HH-PIM achieves up to $60.43$ percent average energy savings over conventional PIMs while meeting application requirements.
  arXiv  Detail & Related papers  (2025-04-02T08:22:32Z)
• COMPASS: A Compiler Framework for Resource-Constrained Crossbar-Array Based In-Memory Deep Learning Accelerators [6.172271429579593]
  We propose a compiler framework for resource-constrained crossbar-based processing-in-memory (PIM) deep neural network (DNN) accelerators. We propose an algorithm to determine the optimal partitioning that divides the layers so that each partition can be accelerated on chip.
  arXiv  Detail & Related papers  (2025-01-12T11:31:25Z)
• OPIMA: Optical Processing-In-Memory for Convolutional Neural Network Acceleration [5.0389804644646174]
  We introduce OPIMA, a processing-in-memory (PIM)-based machine learning accelerator. PIM struggles to achieve high throughput and energy efficiency due to internal data movement bottlenecks. We show that OPIMA can achieve 2.98x higher throughput and 137x better energy efficiency than the best-known prior work.
  arXiv  Detail & Related papers  (2024-07-11T06:12:04Z)
• Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference [2.9302211589186244]
  Large language models (LLMs) have transformed natural language processing, enabling machines to generate human-like text and engage in meaningful conversations. Developments in computing and memory capabilities are lagging behind, exacerbated by the discontinuation of Moore's law. compute-in-memory (CIM) technologies offer a promising solution for accelerating AI inference by directly performing analog computations in memory.
  arXiv  Detail & Related papers  (2024-06-12T16:57:58Z)
• Efficient and accurate neural field reconstruction using resistive memory [52.68088466453264]
  Traditional signal reconstruction methods on digital computers face both software and hardware challenges. We propose a systematic approach with software-hardware co-optimizations for signal reconstruction from sparse inputs. This work advances the AI-driven signal restoration technology and paves the way for future efficient and robust medical AI and 3D vision applications.
  arXiv  Detail & Related papers  (2024-04-15T09:33:09Z)
• Full-Stack Optimization for CAM-Only DNN Inference [2.0837295518447934]
  This paper explores the combination of algorithmic optimizations for ternary weight neural networks and associative processors. We propose a novel compilation flow to optimize convolutions on APs by reducing their arithmetic intensity. Our solution improves the energy efficiency of ResNet-18 inference on ImageNet by 7.5x compared to crossbar in-memory accelerators.
  arXiv  Detail & Related papers  (2024-01-23T10:27:38Z)
• Random resistive memory-based deep extreme point learning machine for unified visual processing [67.51600474104171]
  We propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM) Our co-design system achieves huge energy efficiency improvements and training cost reduction when compared to conventional systems.
  arXiv  Detail & Related papers  (2023-12-14T09:46:16Z)
• EPIM: Efficient Processing-In-Memory Accelerators based on Epitome [78.79382890789607]
  We introduce the Epitome, a lightweight neural operator offering convolution-like functionality. On the software side, we evaluate epitomes' latency and energy on PIM accelerators. We introduce a PIM-aware layer-wise design method to enhance their hardware efficiency.
  arXiv  Detail & Related papers  (2023-11-12T17:56:39Z)
• Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases [9.927754948343326]
  processing-in-memory (PIM) is a promising execution paradigm that alleviates the data movement bottleneck in modern applications. In this paper, we show how to take advantage of the PIM paradigm for two modern data-intensive applications.
  arXiv  Detail & Related papers  (2022-05-29T13:43:17Z)
• Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals [11.31429464715989]
  This paper presents a new PIM architecture to efficiently accelerate deep learning tasks. It is proposed to minimize the required A/D conversions with analog accumulation and neural approximated peripheral circuits. Evaluations on different benchmarks demonstrate that Neural-PIM can improve energy efficiency by 5.36x (1.73x) and speed up throughput by 3.43x (1.59x) without losing accuracy.
  arXiv  Detail & Related papers  (2022-01-30T16:14:49Z)
• SmartDeal: Re-Modeling Deep Network Weights for Efficient Inference and Training [82.35376405568975]
  Deep neural networks (DNNs) come with heavy parameterization, leading to external dynamic random-access memory (DRAM) for storage. We present SmartDeal (SD), an algorithm framework to trade higher-cost memory storage/access for lower-cost computation. We show that SD leads to 10.56x and 4.48x reduction in the storage and training energy, with negligible accuracy loss compared to state-of-the-art training baselines.
  arXiv  Detail & Related papers  (2021-01-04T18:54:07Z)
• To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices [78.38046945665538]
  federated learning (FL) over massive mobile edge devices opens new horizons for numerous intelligent mobile applications. FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training. We develop a convergence-guaranteed FL algorithm enabling flexible communication compression.
  arXiv  Detail & Related papers  (2020-12-22T02:54:18Z)
• One-step regression and classification with crosspoint resistive memory arrays [62.997667081978825]
  High speed, low energy computing machines are in demand to enable real-time artificial intelligence at the edge. One-step learning is supported by simulations of the prediction of the cost of a house in Boston and the training of a 2-layer neural network for MNIST digit recognition. Results are all obtained in one computational step, thanks to the physical, parallel, and analog computing within the crosspoint array.
  arXiv  Detail & Related papers  (2020-05-05T08:00:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.