Automated and Holistic Co-design of Neural Networks and ASICs for Enabling In-Pixel Intelligence

• URL: http://arxiv.org/abs/2407.14560v1
• Date: Thu, 18 Jul 2024 17:58:05 GMT
• Title: Automated and Holistic Co-design of Neural Networks and ASICs for Enabling In-Pixel Intelligence
• Authors: Shubha R. Kharel, Prashansa Mukim, Piotr Maj, Grzegorz W. Deptuch, Shinjae Yoo, Yihui Ren, Soumyajit Mandal,
• Abstract summary: Extreme edge-AI systems, such as those in readout ASICs for radiation detection, must operate under stringent hardware constraints. Finding ideal solutions means identifying optimal AI and ASIC design choices from a design space that has explosively expanded.
• Score: 4.063480188363124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extreme edge-AI systems, such as those in readout ASICs for radiation detection, must operate under stringent hardware constraints such as micron-level dimensions, sub-milliwatt power, and nanosecond-scale speed while providing clear accuracy advantages over traditional architectures. Finding ideal solutions means identifying optimal AI and ASIC design choices from a design space that has explosively expanded during the merger of these domains, creating non-trivial couplings which together act upon a small set of solutions as constraints tighten. It is impractical, if not impossible, to manually determine ideal choices among possibilities that easily exceed billions even in small-size problems. Existing methods to bridge this gap have leveraged theoretical understanding of hardware to f architecture search. However, the assumptions made in computing such theoretical metrics are too idealized to provide sufficient guidance during the difficult search for a practical implementation. Meanwhile, theoretical estimates for many other crucial metrics (like delay) do not even exist and are similarly variable, dependent on parameters of the process design kit (PDK). To address these challenges, we present a study that employs intelligent search using multi-objective Bayesian optimization, integrating both neural network search and ASIC synthesis in the loop. This approach provides reliable feedback on the collective impact of all cross-domain design choices. We showcase the effectiveness of our approach by finding several Pareto-optimal design choices for effective and efficient neural networks that perform real-time feature extraction from input pulses within the individual pixels of a readout ASIC.

Related papers

• LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization [48.41286573672824]
  Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of the human brain and are highly energy-efficient. We propose a new approach named LitE-SNN that incorporates both spatial and temporal compression into the automated network design process.
  arXiv  Detail & Related papers  (2024-01-26T05:23:11Z)
• OFA$^2$: A Multi-Objective Perspective for the Once-for-All Neural Architecture Search [79.36688444492405]
  Once-for-All (OFA) is a Neural Architecture Search (NAS) framework designed to address the problem of searching efficient architectures for devices with different resources constraints. We aim to give one step further in the search for efficiency by explicitly conceiving the search stage as a multi-objective optimization problem.
  arXiv  Detail & Related papers  (2023-03-23T21:30:29Z)
• Approaching Globally Optimal Energy Efficiency in Interference Networks via Machine Learning [22.926877147296594]
  This work presents a machine learning approach to optimize the energy efficiency (EE) in a multi-cell wireless network. Results show that the method achieves an EE close to the optimum by the branch-and- computation testing.
  arXiv  Detail & Related papers  (2022-11-25T08:36:34Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• FreeREA: Training-Free Evolution-based Architecture Search [17.202375422110553]
  FreeREA is a custom cell-based evolution NAS algorithm that exploits an optimised combination of training-free metrics to rank architectures. Our experiments, carried out on the common benchmarks NAS-Bench-101 and NATS-Bench, demonstrate that i) FreeREA is a fast, efficient, and effective search method for models automatic design.
  arXiv  Detail & Related papers  (2022-06-17T11:16:28Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• A Graph Deep Learning Framework for High-Level Synthesis Design Space Exploration [11.154086943903696]
  High-Level Synthesis is a solution for fast prototyping application-specific hardware. We propose HLS, for the first time in the literature, graph neural networks that jointly predict acceleration performance and hardware costs. We show that our approach achieves prediction accuracy comparable with that of commonly used simulators.
  arXiv  Detail & Related papers  (2021-11-29T18:17:45Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures [7.085772863979686]
  Deep neural networks (DNNs) have led to large strides in various Artificial Intelligence (AI) inference tasks, such as object and speech recognition. deploying such AI models across commodity devices faces significant challenges. We present techniques for achieving real-time performance following a cross-stack approach.
  arXiv  Detail & Related papers  (2021-06-21T11:23:12Z)
• Data-Driven Random Access Optimization in Multi-Cell IoT Networks with NOMA [78.60275748518589]
  Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve the random access efficiency in high-density spatially-distributed multi-cell wireless IoT networks. A novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity.
  arXiv  Detail & Related papers  (2021-01-02T15:21:08Z)

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.