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Amir Aminifar

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7 papers

2 author rows

ECAI Conference 2025 Conference Paper

Formal Local Implication Between Two Neural Networks

Anahita Baninajjar
Ahmed Rezine
Amir Aminifar

Given two neural network classifiers with the same input and output domains, our goal is to compare the two networks in relation to each other over an entire input region (e. g. , within a vicinity of an input sample). To this end, we establish the foundation of formal local implication between two networks, i. e. , N2 ⇒D N1, in an entire input region D. That is, network N1 consistently makes a correct decision every time network N2 does, and it does so in an entire input region D. We further propose a sound formulation for establishing such formally-verified (provably correct) local implications. The proposed formulation is relevant in the context of several application domains, e. g. , for comparing a trained network and its corresponding compact (e. g. , pruned, quantized, distilled) networks. We evaluate our formulation based on the MNIST, CIFAR10, and two real-world medical datasets, to show its relevance.

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AAAI Conference 2025 Conference Paper

TinyFoA: Memory Efficient Forward-Only Algorithm for On-Device Learning

Baichuan Huang
Amir Aminifar

Forward-only algorithms offer a promising memory-efficient alternative to Backpropagation (BP) for on-device learning. However, state-of-the-art forward-only algorithms, e.g., Forward-Forward (FF), still require a substantial amount of memory during the training process, often exceeding the limits of mobile edge and Internet of Things (IoT) devices. At the same time, existing memory-optimization techniques, e.g., binarizing parameters and activations, are mainly designed for BP, hence significantly degrading the classification performance when applied to state-of-the-art forward-only algorithms. In this paper, we propose a memory-efficient forward-only algorithm called TinyFoA, to reduce dynamic memory overhead in the training process. Our TinyFoA optimizes the memory efficiency not only by layer-wise training but also by partially updating each layer, as well as by binarizing the weights and the activations. We extensively evaluate our proposed TinyFoA against BP and other forward-only algorithms and demonstrate its effectiveness and superiority compared to state-of-the-art forward-only algorithms in terms of classification performance and training memory overhead, reducing the memory overheads by an order of magnitude.

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ECAI Conference 2024 Conference Paper

LightFF: Lightweight Inference for Forward-Forward Algorithm

Amin Aminifar
Baichuan Huang
Azra Abtahi
Amir Aminifar

The human brain performs tasks with an outstanding energy efficiency, i. e. , with approximately 20 Watts. The state-of-the-art Artificial/Deep Neural Networks (ANN/DNN), on the other hand, have recently been shown to consume massive amounts of energy. The training of these ANNs/DNNs is done almost exclusively based on the back-propagation algorithm, which is known to be biologically implausible. This has led to a new generation of forward-only techniques, including the Forward-Forward algorithm. In this paper, we propose a lightweight inference scheme specifically designed for DNNs trained using the Forward-Forward algorithm. We have evaluated our proposed lightweight inference scheme in the case of the MNIST and CIFAR datasets, as well as two real-world applications, namely, epileptic seizure detection and cardiac arrhythmia classification using wearable technologies, where complexity overheads/energy consumption is a major constraint, and demonstrate its relevance. Our code is available at https: //github. com/AminAminifar/LightFF.

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TIST Journal 2024 Journal Article

M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems

Saleh Baghersalimi
Alireza Amirshahi
Farnaz Forooghifar
Tomas Teijeiro
Amir Aminifar
David Atienza

Integrating low-power wearable systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables make it possible to target complex scenarios by exploiting multiple biosignals and using high-performance algorithms, such as Deep Neural Networks (DNNs). However, there is a tradeoff between the algorithms’ performance and the low-power requirements of platforms with limited resources. Besides, physically larger and multi-biosignal-based wearables bring significant discomfort to the patients. Consequently, reducing power consumption and discomfort is necessary for patients to use wearable devices continuously during everyday life. To overcome these challenges, in the context of epileptic seizure detection, we propose the Multi-to-Single Knowledge Distillation (M2SKD) approach targeting single-biosignal processing in wearable systems. The starting point is to train a highly-accurate multi-biosignal DNN, then apply M2SKD to develop a single-biosignal DNN solution for wearable systems that achieves an accuracy comparable to the original multi-biosignal DNN. To assess the practicality of our approach to real-life scenarios, we perform a comprehensive simulation experiment analysis on several edge computing platforms.

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ICML Conference 2024 Conference Paper

VNN: Verification-Friendly Neural Networks with Hard Robustness Guarantees

Anahita Baninajjar
Ahmed Rezine
Amir Aminifar

Machine learning techniques often lack formal correctness guarantees, evidenced by the widespread adversarial examples that plague most deep-learning applications. This lack of formal guarantees resulted in several research efforts that aim at verifying Deep Neural Networks (DNNs), with a particular focus on safety-critical applications. However, formal verification techniques still face major scalability and precision challenges. The over-approximation introduced during the formal verification process to tackle the scalability challenge often results in inconclusive analysis. To address this challenge, we propose a novel framework to generate Verification-Friendly Neural Networks (VNNs). We present a post-training optimization framework to achieve a balance between preserving prediction performance and verification-friendliness. Our proposed framework results in VNNs that are comparable to the original DNNs in terms of prediction performance, while amenable to formal verification techniques. This essentially enables us to establish robustness for more VNNs than their DNN counterparts, in a time-efficient manner.

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JBHI Journal 2023 Journal Article

M2D2: Maximum-Mean-Discrepancy Decoder for Temporal Localization of Epileptic Brain Activities

Alireza Amirshahi
Anthony Thomas
Amir Aminifar
Tajana Rosing
David Atienza

Recent years have seen growing interest in leveraging deep learning models for monitoring epilepsy patients based on electroencephalographic (EEG) signals. However, these approaches often exhibit poor generalization when applied outside of the setting in which training data was collected. Furthermore, manual labeling of EEG signals is a time-consuming process requiring expert analysis, making fine-tuning patient-specific models to new settings a costly proposition. In this work, we propose the Maximum-Mean-Discrepancy Decoder (M2D2) for automatic temporal localization and labeling of seizures in long EEG recordings to assist medical experts. We show that M2D2 achieves 76. 0% and 70. 4% of F1-score for temporal localization when evaluated on EEG data gathered in a different clinical setting than the training data. The results demonstrate that M2D2 yields substantially higher generalization performance than other state-of-the-art deep learning-based approaches.

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JBHI Journal 2022 Journal Article

Personalized Real-Time Federated Learning for Epileptic Seizure Detection

Saleh Baghersalimi
Tomas Teijeiro
David Atienza
Amir Aminifar

Epilepsy is one of the most prevalent paroxystic neurological disorders. It is characterized by the occurrence of spontaneous seizures. About 1 out of 3 patients have drug-resistant epilepsy, thus their seizures cannot be controlled by medication. Automatic detection of epileptic seizures can substantially improve the patient’s quality of life. To achieve a high-quality model, we have to collect data from various patients in a central server. However, sending the patient’s raw data to this central server puts patient privacy at risk and consumes a significant amount of energy. To address these challenges, in this work, we have designed and evaluated a standard federated learning framework in the context of epileptic seizure detection using a deep learning-based approach, which operates across a cluster of machines. We evaluated the accuracy and performance of our proposed approach on the NVIDIA Jetson Nano Developer Kit based on the EPILEPSIAE database, which is one of the largest public epilepsy datasets for seizure detection. Our proposed framework achieved a sensitivity of 81. 25%, a specificity of 82. 00%, and a geometric mean of 81. 62%. It can be implemented on embedded platforms that complete the entire training process in 1. 86 hours using 344. 34 mAh energy on a single battery charge. We also studied a personalized variant of the federated learning, where each machine is responsible for training a deep neural network (DNN) to learn the discriminative electrocardiography (ECG) features of the epileptic seizures of the specific person monitored based on its local data. In this context, the DNN benefitted from a well-trained model without sharing the patient’s raw data with a server or a central cloud repository. We observe in our results that personalized federated learning provides an increase in all the performance metric, with a sensitivity of 90. 24%, a specificity of 91. 58%, and a geometric mean of 90. 90%.

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