Accelerating DNNs using Weight Clustering on RISC-V Custom Functional Units - Sabih, Muhammad; Sesli, Batuhan; Hannig, Frank; Teich, Jürgen (Proceedings of the Conference on Design, Automation and Test in Europe (DATE), 2024)

Beyond BNNs: Design and Acceleration of Sub-Bit Neural Networks using RISC-V Custom Functional Units - Sabih, Muhammad; Abdo, Mohamed; Hannig, Frank; Teich, Jürgen (CASES: International Conference on Compilers, Architectures, and Synthesis for Embedded Systems, 2025)
Binary Neural Networks (BNNs) are known for their minimal memory requirements, making them an attractive choice for resource-constrained environments. Sub-Bit Neural Networks (SBNNs) are a more recent advancement that extend the benefits of BNNs by compressing them even further, achieving sub-bit level representations to maximize efficiency. However, effectively compressing and accelerating BNNs presents challenges. In this paper, we propose a novel approach to compress BNNs using a fixed-length compression scheme that can be efficiently decoded at runtime. We then propose RISC-V extensions, implemented as a Custom Functional Unit (CFU), to decode compressed weights via a codebook stored on an FPGA on-board memory, followed by XOR and population count operations. This approach achieves a speedup of up to 2× compared to conventional BNNs deployed on the RISC-V softcore, with significantly less accuracy degradation, and provides a foundation for exploring even higher compression configurations to improve performance furthe

Design of Machine Learning Accelerators as RISC-V Extensions using an Open Source Tool Flow - Sesli, Batuhan; Sabih, Muhammad; Hannig, Frank; Teich, Jürgen (Proceedings of the International Conference on Computer Aided Design (ICCAD), 2025)

DyFiP: Explainable AI-based Dynamic Filter Pruning of Convolutional Neural Networks - Sabih, Muhammad; Hannig, Frank; Teich, Jürgen (Proceedings of the 2nd European Workshop on Machine Learning and Systems (EuroMLSys), 2022)
Filter pruning is one of the most effective ways to accelerate Convolutional Neural Networks (CNNs). Most of the existing works are focused on the static pruning of CNN filters. In dynamic pruning of CNN filters, existing works are based on the idea of switching between different branches of a CNN or exiting early based on the hardness of a sample. These approaches can reduce the average latency of inference, but they cannot reduce the longest-path latency of inference. In contrast, we present a novel approach of dynamic filter pruning that utilizes explainable AI along with early coarse prediction in the intermediate layers of a CNN. This coarse prediction is performed using a simple branch that is trained to perform top-k classification. The branch either predicts the output class with high confidence, in which case the rest of the computations are left out. Alternatively, the branch predicts the output class to be within a subset of possible output classes. After this coarse prediction, only those filters that are important for this subset of classes are then evaluated. The importances of filters for each output class are obtained using explainable AI. Using this concept of dynamic pruning, we are able not only to reduce the average latency of inference, but also the longest-path latency of inference. Our proposed architecture for dynamic pruning can be deployed on different hardware platform
Keywords: Explainable AI; Filter Pruning; CNN; Dynamic Pruning

Fault-Tolerant Low-Precision DNNs using Explainable AI - Sabih, Muhammad; Hannig, Frank; Teich, Jürgen (2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 2021)
Hardware-efficient machine learning systems deployed in safety-critical systems need to be optimized for two conflicting objectives. One is making neural networks more compact and efficient by techniques such as quantization and pruning. The other is making them robust against faults. Robustness of Deep Neural Networks (DNNs) becomes a challenge in low-bit precision neural networks such as networks quantized with 8-bit integer precision or less and on custom DNN accelerators with emerging memory technologies and techniques such as approximate memory. Errors in the processing of DNNs can be modeled as bit-flips at the software level. These bit-flips can be caused by persistent memory errors, manifesting themselves in corrupted DNN weights, or they can be caused by transient soft errors.In this work, we introduce an open-source fault-injection framework to simulate both persistent errors and transient errors on PyTorch. Subsequently, we propose novel algorithms that utilize explainable AI methods to make DNNs robust against both kinds, i.e., persistent memory errors and transient soft errors, while keeping the overhead small. We show that our approach can be beneficially used here to mitigate (1) persistent errors by identifying important bits in weights and selectively protecting these using error correcting codes, and (2) transient errors by identifying important samples in activations and selectively protecting these using Triple Modular Redundancy (TMR). Our proposed method outperforms the previously known work on selectively protecting DNN bits in terms of performance and creates further opportunities for research in the area of utilizing explainable AI for robustness.<br
Keywords: Explainable AI; reliable DNNs; robust DNNs; Sensitivity; Quantization (signal); Neural networks; Memory management; Software algorithms; Redundancy; Robustness

Hardware-Aware Evolutionary Explainable Filter Pruning for Convolutional Neural Networks - Heidorn, Christian; Sabih, Muhammad; Meyerhöfer, Nicolai; Schinabeck, Christian; Teich, Jürgen; Hannig, Frank (International Journal of Parallel Programming, 2024)
Filter pruning of convolutional neural networks (CNNs) is a common technique to effectively reduce the memory footprint, the number of arithmetic operations, and, consequently, inference time. Recent pruning approaches also consider the targeted device (i.e., graphics processing units) for CNN deployment to reduce the actual inference time. However, simple metrics, such as the L1-norm, are used for deciding which filters to prune. In this work, we propose a hardware-aware technique to explore the vast multi-objective design space of possible filter pruning configurations. Our approach incorporates not only the targeted device but also techniques from explainable artificial intelligence for ranking and deciding which filters to prune. For each layer, the number of filters to be pruned is optimized with the objective of minimizing the inference time and the error rate of the CNN. Experimental results show that our approach can speed up inference time by 1.40× and 1.30× for VGG-16 on the CIFAR-10 dataset and ResNet-18 on the ILSVRC-2012 dataset, respectively, compared to the state-of-the-art ABCPruner.<br
Keywords: Filter pruning; Evolutionary algorithm; Explainable AI

Hardware/Software Co-Design of RISC-V Extensions for Accelerating Sparse DNNs on FPGAs - Sabih, Muhammad; Karim, Abrarul; Wittmann, Jakob; Hannig, Frank; Teich, Jürgen (Proceedings of International Conference on Field Programmable Technology (FPT), 2024)

MOSP: Multi-Objective Sensitivity Pruning of Deep Neural Networks - Mishra, Ashutosh; Hannig, Frank; Teich, Jürgen; Sabih, Muhammad (2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC), 2022)
Deep neural networks (DNNs) are computationally intensive, making them difficult to deploy on resource-constrained embedded systems. Model compression is a set of techniques that removes redundancies from a neural network with affordable degradation in task performance. Most compression methods do not target hardware-based objectives such as latency directly; however, few methods approximate latency with floating-point operations (FLOPs) or multiply-accumulate operations (MACs). Using these indirect metrics cannot directly translate to the relevant performance metric on the hardware, i.e., latency and throughput. To address this limitation, we introduce Multi-Objective Sensitivity Pruning, “MOSP,” a three-stage pipeline for filter pruning: hardware-aware sensitivity analysis, Criteria-optimal configuration selection, and pruning based on explainable AI (XAI). Our pipeline is compatible with a single or combination of target objectives such as latency, energy consumption, and accuracy. Our method first formulates the sensitivity of layers of a model against the target objectives as a classical machine learning problem. Next, we choose a Criteria-optimal configuration controlled by hyperparameters specific to each objective of choice. Finally, we apply XAI-based filter ranking to select filters to be pruned. The pipeline follows an iterative pruning methodology to recover any loss in degradation in task performance (e.g., accuracy). We allow the user to prefer one objective function over the other. Our method outperforms the selected baseline method across different neural networks and datasets in both accuracy and latency reductions and is competitive with state-of-the-art approaches.<br
Keywords: Compression; Pruning; Energy Efficiency; Deep Neural Networks; Structured Pruning; Multi-Objective compression

Multi-Partner Project: A Holistic and Open-Source Approach to Efficient, Secure and Reliable AI Hardware Deployment in DI-EDAI - Sotiropoulos, Georgios; Frombach, Felix; Höfer, Julian; Harbaum, Tanja; Becker, Jürgen; Thorøe, Henrik Iver; Meyers, Vincent; Tahoori, Mehdi; Demirdag, Zeynep; Sikal, Mohammed Bakr; Nassar, Hassan; Khdr, Heba; et al. (Proceedings of the Conference on Design, Automation and Test in Europe (DATE), 2026)

Robust and Tiny Binary Neural Networks using Gradient-based Explainability Methods - Sabih, Muhammad; Yayla, Mikail; Hannig, Frank; Teich, Jürgen; Chen, Jian-Jia (EuroMLSys '23: Proceedings of the 3rd Workshop on Machine Learning and System, 2023)
Binary neural networks (BNNs) are a highly resource-efficient variant of neural networks. The efficiency of BNNs for tiny machine learning (TinyML) systems can be enhanced by structured pruning and making BNNs robust to faults. When used with approximate memory systems, this fault tolerance can be traded off for energy consumption, latency, or cost. For pruning, magnitude-based heuristics are not useful because the weights in a BNN can either be -1 or +1. Global pruning of BNNs has not been studied well so far. Thus, in this paper, we explore gradient-based ranking criteria for pruning BNNs and use them in combination with a sensitivity analysis. For robustness, the state-of-the-art is to train the BNNs with bit-flips in what is known as fault-aware training. We propose a method to guide fault-aware training using gradient-based explainability methods. This allows us to obtain robust and efficient BNNs for deployment on tiny devices. Experiments on audio and image processing applications show that our proposed approach outperforms the existing approaches, making it useful for obtaining efficient and robust models for a slight degradation in accuracy. This makes our approach valuable for many TinyML use cases.<br
Keywords: Robustness; Explainable-AI; BNNs; DNNs; Binary Neural Networks

Special Session – Hardware-Software Co-Design for Machine Learning Systems Made Open-Source - Tahoori, Mehdi; Meyers, Vincent; Sadeghipour Roodsari, Mahboobe; Xu, Huashuangyang; Becker, Jürgen; Harbaum, Tanja; Frombach, Felix; Höfer, Julian; Sotiropoulos, Georgios; Henkel, Jörg; Demirdag, Zeynep; Khdr, Heba; et al. (Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), 2025)

Utilizing Explainable AI for Quantization and Pruning of Deep Neural Networks - Sabih, Muhammad; Hannig, Frank; Teich, Jürgen (2020)
For many applications, utilizing Deep Neural Networks (DNNs) requires their implementation on a target architecture in an optimized manner concerning energy consumption, memory requirement, throughput, etc. DNN compression is used to reduce the memory footprint and complexity of a DNN before its deployment on hardware. Recent efforts to understand and explain AI (Artificial Intelligence)methods have led to a new research area, termed as explainable AI. Explainable AI methods allow us to better understand the inner working of DNNs, such as the importance of different neurons and features. The concepts from explainable AI provide an opportunity to improve DNN compression methods such as quantization and pruning in several ways that have not been sufficiently explored so far. In this paper, we utilize explainable AI methods: mainly DeepLIFT method. We use DeepLIFT for (1) pruning of DNNs; this includes structured and unstructured pruning of Convolutional Neural Network (CNN) filters pruning as well as pruning weights of fully connected layers, (2) non-uniform quantization of DNN weights using clustering algorithm; this is also referred to as Weight Sharing, and (3)integer-based mixed-precision quantization; this is where each layer of a DNN may use a different number of integer bits. We use typical image classification datasets with common deep learning image classification models for evaluation. In all these three cases, we demonstrate significant improvements as well as new insights and opportunities from the use of explainable AI in DNN compressio