Evolutionary Design of the Memory Subsystem

• URL: http://arxiv.org/abs/2303.16074v1
• Date: Tue, 7 Mar 2023 10:45:51 GMT
• Title: Evolutionary Design of the Memory Subsystem
• Authors: Josefa D\'iaz \'Alvarez, Jos\'e L. Risco-Mart\'in and J. Manuel Colmenar
• Abstract summary: We address the optimization of the whole memory subsystem with three approaches integrated as a single methodology. To this aim, we apply different evolutionary algorithms in combination with memory simulators and profiling tools. We also provide an experimental experience where our proposal is assessed using well-known benchmark applications.
• Score: 2.378428291297535
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The memory hierarchy has a high impact on the performance and power consumption in the system. Moreover, current embedded systems, included in mobile devices, are specifically designed to run multimedia applications, which are memory intensive. This increases the pressure on the memory subsystem and affects the performance and energy consumption. In this regard, the thermal problems, performance degradation and high energy consumption, can cause irreversible damage to the devices. We address the optimization of the whole memory subsystem with three approaches integrated as a single methodology. Firstly, the thermal impact of register file is analyzed and optimized. Secondly, the cache memory is addressed by optimizing cache configuration according to running applications and improving both performance and power consumption. Finally, we simplify the design and evaluation process of general-purpose and customized dynamic memory manager, in the main memory. To this aim, we apply different evolutionary algorithms in combination with memory simulators and profiling tools. This way, we are able to evaluate the quality of each candidate solution and take advantage of the exploration of solutions given by the optimization algorithm.We also provide an experimental experience where our proposal is assessed using well-known benchmark applications.

Related papers

• Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning [64.93848182403116]
  Current deep-learning memory models struggle in reinforcement learning environments that are partially observable and long-term. We introduce the Stable Hadamard Memory, a novel memory model for reinforcement learning agents. Our approach significantly outperforms state-of-the-art memory-based methods on challenging partially observable benchmarks.
  arXiv  Detail & Related papers  (2024-10-14T03:50:17Z)
• CHIME: Energy-Efficient STT-RAM-based Concurrent Hierarchical In-Memory Processing [1.5566524830295307]
  This paper introduces a novel PiC/PiM architecture, Concurrent Hierarchical In-Memory Processing (CHIME) CHIME strategically incorporates heterogeneous compute units across multiple levels of the memory hierarchy. Experiments reveal that, compared to the state-of-the-art bit-line computing approaches, CHIME achieves significant speedup and energy savings of 57.95% and 78.23%.
  arXiv  Detail & Related papers  (2024-07-29T01:17:54Z)
• A parallel evolutionary algorithm to optimize dynamic memory managers in embedded systems [4.651702738999686]
  We present a novel parallel evolutionary algorithm for DMMs optimization in embedded systems. Our framework is able to reach a speed-up of 86.40x when compared with other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-06-28T15:47:25Z)
• 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)
• Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures [68.91874045918112]
  adapter-ALBERT is an efficient model optimization for maximal data reuse across different tasks. We demonstrate the advantage of mapping the model to a heterogeneous on-chip memory architecture by performing simulations on a validated NLP edge accelerator.
  arXiv  Detail & Related papers  (2023-03-25T14:40:59Z)
• Performance Embeddings: A Similarity-based Approach to Automatic Performance Optimization [71.69092462147292]
  Performance embeddings enable knowledge transfer of performance tuning between applications. We demonstrate this transfer tuning approach on case studies in deep neural networks, dense and sparse linear algebra compositions, and numerical weather prediction stencils.
  arXiv  Detail & Related papers  (2023-03-14T15:51:35Z)
• Optimizing L1 cache for embedded systems through grammatical evolution [1.9371782627708491]
  Grammatical Evolution (GE) is able to efficiently find the best cache configurations for a given set of benchmark applications. Our proposal is able to find cache configurations that obtain an average improvement of $62%$ versus a real world baseline configuration.
  arXiv  Detail & Related papers  (2023-03-06T18:10:00Z)
• Multi-objective optimization of energy consumption and execution time in a single level cache memory for embedded systems [2.378428291297535]
  Multi-objective optimization may help to minimize both conflicting metrics in an independent manner. Our design method reaches an average improvement of 64.43% and 91.69% in execution time and energy consumption.
  arXiv  Detail & Related papers  (2023-02-22T09:35:03Z)
• 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)
• Recurrent Dynamic Embedding for Video Object Segmentation [54.52527157232795]
  We propose a Recurrent Dynamic Embedding (RDE) to build a memory bank of constant size. We propose an unbiased guidance loss during the training stage, which makes SAM more robust in long videos. We also design a novel self-correction strategy so that the network can repair the embeddings of masks with different qualities in the memory bank.
  arXiv  Detail & Related papers  (2022-05-08T02:24:43Z)
• Memformer: A Memory-Augmented Transformer for Sequence Modeling [55.780849185884996]
  We present Memformer, an efficient neural network for sequence modeling. Our model achieves linear time complexity and constant memory space complexity when processing long sequences.
  arXiv  Detail & Related papers  (2020-10-14T09:03:36Z)

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.