Retrospective: A CORDIC Based Configurable Activation Function for NN Applications

• URL: http://arxiv.org/abs/2503.14354v1
• Date: Tue, 18 Mar 2025 15:38:37 GMT
• Title: Retrospective: A CORDIC Based Configurable Activation Function for NN Applications
• Abstract summary: CORDIC-based configuration for the design of Activation Functions (AF) was previously suggested to accelerate ASIC hardware design for resource-constrained systems.<n>We introduce the DA-VINCI AF tailored for the evolving needs of AI applications.
• Score: 1.3133581869733064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A CORDIC-based configuration for the design of Activation Functions (AF) was previously suggested to accelerate ASIC hardware design for resource-constrained systems by providing functional reconfigurability. Since its introduction, this new approach for neural network acceleration has gained widespread popularity, influencing numerous designs for activation functions in both academic and commercial AI processors. In this retrospective analysis, we explore the foundational aspects of this initiative, summarize key developments over recent years, and introduce the DA-VINCI AF tailored for the evolving needs of AI applications. This new generation of dynamically configurable and precision-adjustable activation function cores promise greater adaptability for a range of activation functions in AI workloads, including Swish, SoftMax, SeLU, and GeLU, utilizing the Shift-and-Add CORDIC technique. The previously presented design has been optimized for MAC, Sigmoid, and Tanh functionalities and incorporated into ReLU AFs, culminating in an accumulative NEURIC compute unit. These enhancements position NEURIC as a fundamental component in the resource-efficient vector engine for the realization of AI accelerators that focus on DNNs, RNNs/LSTMs, and Transformers, achieving a quality of results (QoR) of 98.5%.

Related papers

• Optimizing Neural Networks with Learnable Non-Linear Activation Functions via Lookup-Based FPGA Acceleration [17.92095380908621]
  FPGA-based design achieves superior computational speed and over $104$ times higher energy efficiency compared to edge CPUs and GPU.<n>This breakthrough positions our approach as a practical enabler for energy-critical edge AI, where computational intensity and power constraints traditionally preclude the use of adaptive activation networks.
  arXiv  Detail & Related papers  (2025-08-23T15:51:14Z)
• Task-Specific Activation Functions for Neuroevolution using Grammatical Evolution [0.0]
  We introduce Neuvo GEAF - an innovative approach leveraging grammatical evolution (GE) to automatically evolve novel activation functions.<n>Experiments conducted on well-known binary classification datasets show statistically significant improvements in F1-score (between 2.4% and 9.4%) over ReLU.
  arXiv  Detail & Related papers  (2025-03-13T20:50:21Z)
• Enhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGA [20.629635991749808]
  This paper proposes an algorithm and hardware co-design framework that can generate field-programmable gate array (FPGA)-based accelerators for efficient BayesNNs. At the algorithm level, we propose novel multi-exit dropout-based BayesNNs with reduced computational and memory overheads. At the hardware level, this paper introduces a transformation framework that can generate FPGA-based accelerators for the proposed efficient BayesNNs.
  arXiv  Detail & Related papers  (2024-06-20T17:08:42Z)
• DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
  We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
  arXiv  Detail & Related papers  (2024-06-11T06:31:03Z)
• Pointer Networks with Q-Learning for Combinatorial Optimization [55.2480439325792]
  We introduce the Pointer Q-Network (PQN), a hybrid neural architecture that integrates model-free Q-value policy approximation with Pointer Networks (Ptr-Nets) Our empirical results demonstrate the efficacy of this approach, also testing the model in unstable environments.
  arXiv  Detail & Related papers  (2023-11-05T12:03:58Z)
• Generalized Activation via Multivariate Projection [46.837481855573145]
  Activation functions are essential to introduce nonlinearity into neural networks. We consider ReLU as a projection from R onto the nonnegative half-line R+. We extend ReLU by substituting it with a generalized projection operator onto a convex cone, such as the Second-Order Cone (SOC) projection.
  arXiv  Detail & Related papers  (2023-09-29T12:44:27Z)
• Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation [49.44309457870649]
  We present Layer-wise Feedback Propagation (LFP), a novel training principle for neural network-like predictors.<n>LFP decomposes a reward to individual neurons based on their respective contributions to solving a given task.<n>Our method then implements a greedy approach reinforcing helpful parts of the network and weakening harmful ones.
  arXiv  Detail & Related papers  (2023-08-23T10:48:28Z)
• LTC-SE: Expanding the Potential of Liquid Time-Constant Neural Networks for Scalable AI and Embedded Systems [0.0]
  We present an improved version of the Liquid Time-Constant neural network algorithm originally proposed by Hasani et al. in 2021. The enhancements in LTC-SE focus on augmenting flexibility, compatibility, and code organization. These refinements expand the applicability of liquid neural networks in diverse machine learning tasks, such as robotics, causality analysis, and time-series prediction.
  arXiv  Detail & Related papers  (2023-04-18T01:57:10Z)
• Efficient Activation Function Optimization through Surrogate Modeling [15.219959721479835]
  This paper aims to improve the state of the art through three steps. First, the benchmark Act-Bench-CNN, Act-Bench-ResNet, and Act-Bench-ViT were created by training convolutional, residual, and vision transformer architectures. Second, a characterization of the benchmark space was developed, leading to a new surrogate-based method for optimization.
  arXiv  Detail & Related papers  (2023-01-13T23:11:14Z)
• Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs [74.83613252825754]
  "smart ecosystems" are being formed where sensing happens concurrently rather than standalone. This is shifting the on-device inference paradigm towards deploying neural processing units (NPUs) at the edge. We propose a novel early-exit scheduling that allows preemption at run time to account for the dynamicity introduced by the arrival and exiting processes.
  arXiv  Detail & Related papers  (2022-09-27T15:04:01Z)
• FPGA-optimized Hardware acceleration for Spiking Neural Networks [69.49429223251178]
  This work presents the development of a hardware accelerator for an SNN, with off-line training, applied to an image recognition task. The design targets a Xilinx Artix-7 FPGA, using in total around the 40% of the available hardware resources. It reduces the classification time by three orders of magnitude, with a small 4.5% impact on the accuracy, if compared to its software, full precision counterpart.
  arXiv  Detail & Related papers  (2022-01-18T13:59:22Z)
• AutoBERT-Zero: Evolving BERT Backbone from Scratch [94.89102524181986]
  We propose an Operation-Priority Neural Architecture Search (OP-NAS) algorithm to automatically search for promising hybrid backbone architectures. We optimize both the search algorithm and evaluation of candidate models to boost the efficiency of our proposed OP-NAS. Experiments show that the searched architecture (named AutoBERT-Zero) significantly outperforms BERT and its variants of different model capacities in various downstream tasks.
  arXiv  Detail & Related papers  (2021-07-15T16:46:01Z)
• Regularized Flexible Activation Function Combinations for Deep Neural Networks [25.129423369243284]
  It has been shown that LSTM models with proposed flexible activations P-Sig-Ramp provide significant improvements in time series forecasting. The proposed P-E2-ReLU achieves better and more stable performance on lossy image compression tasks with convolutional auto-encoders.
  arXiv  Detail & Related papers  (2020-07-26T11:32:52Z)
• Discovering Parametric Activation Functions [17.369163074697475]
  This paper proposes a technique for customizing activation functions automatically, resulting in reliable improvements in performance. Experiments with four different neural network architectures on the CIFAR-10 and CIFAR-100 image classification datasets show that this approach is effective.
  arXiv  Detail & Related papers  (2020-06-05T00:25:33Z)
• Evolutionary Optimization of Deep Learning Activation Functions [15.628118691027328]
  We show that evolutionary algorithms can discover novel activation functions that outperform the Rectified Linear Unit (ReLU) replacing ReLU with evolved activation functions results in statistically significant increases in network accuracy. These novel activation functions are shown to generalize, achieving high performance across tasks.
  arXiv  Detail & Related papers  (2020-02-17T19:54:26Z)

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.