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All Journal International Journal of Sustainable Engineering Innovations
Anwar Ali Sathio
Benazir Bhutto Shaheed University Karachi
Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Neuromorphic Computing Chips for Edge AI: A Comprehensive Analysis of Brain-Inspired Hardware Architecture for Real-Time Intelligent Systems Anwar Ali Sathio; Chiragh Kumar Maheshwari
International Journal of Sustainable Engineering Innovations Vol. 2 No. 1 (2026): International Journal of Sustainable Engineering Innovations (February 2026)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijsei.v2i1.151

Abstract

Background of study: Edge computing devices like autonomous robots and IoT sensors need sophisticated AI for real-time decisions, but conventional processors consume 15-300 watts during inference, creating critical limitations for battery-powered deployments. GPU-based accelerators face memory bottlenecks and high energy costs from data movement, making sustained autonomous operation impractical. Aims of paper: This research compares neuromorphic platforms (Intel Loihi 2, IBM TrueNorth, BrainChip Akida) against conventional accelerators (NVIDIA Jetson, Google Coral) to evaluate if neuromorphic architectures can solve edge AI energy efficiency challenges across five representative workloads. Methods: Using an experimental design with hardware benchmarking and power analysis, we evaluated five edge AI workloads. ANOVA and regression modeling were then applied to rigorously compare computing paradigms while controlling for variables. Result: Neuromorphic platforms demonstrated 15-50× improved energy efficiency versus conventional GPU accelerators for event-driven workloads. Intel Loihi 2 achieved 2,400 inferences/joule at 1.8 watts versus 180 inferences/joule at 18.5 watts for NVIDIA Jetson. IBM TrueNorth operated at 70 milliwatts for pattern recognition. BrainChip Akida achieved 94.6% accuracy on keyword spotting at 0.8 watts. Event-driven processing exhibited 0.4ms latency versus 5.1ms for frame-based systems. Neuromorphic chips maintained stable performance without active cooling below 65°C, while conventional accelerators required thermal management above 85°C. Conclusion: Neuromorphic processors (0.6-5W) excel in power-efficient edge AI for event-driven data. While hybrid architectures optimize performance, adoption is hindered by immature software ecosystems, limited training frameworks, and a 2-4% accuracy gap compared to conventional methods.
Forest Fire Alert System Using Satellite Imagery, Machine Learning, and GPS-Based Early Warning Mechanism Anwar Ali Sathio; Doulat Ram; Abdul Aziz Khan; Sameer Ali
International Journal of Sustainable Engineering Innovations Vol. 2 No. 1 (2026): International Journal of Sustainable Engineering Innovations (February 2026)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijsei.v2i1.152

Abstract

Background of study: Wildfires pose a critical threat to global ecosystems, biodiversity, and human safety, with climate change intensifying fire frequency, scale, and unpredictability. Traditional wildfire detection approaches often suffer from delayed response, limited coverage, and insufficient automation, which restrict effective mitigation and early intervention. Aims and scope of paper: This paper aims to design and evaluate an intelligent Forest Fire Alert System that integrates satellite remote sensing, machine learning models, Internet of Things based environmental sensing, and real time alert communication to enable early wildfire detection and proactive risk assessment. Methods: The proposed system employs a Convolutional Neural Network to detect active fire regions from multispectral satellite imagery, while a Random Forest classifier estimates wildfire risk levels based on meteorological variables and IoT sensor data. Geospatial positioning through GPS supports precise location mapping, and a web-based dashboard disseminates real time alerts to forestry authorities for rapid response. Result: Experimental evaluation demonstrates strong performance of the proposed framework. The CNN model achieved an accuracy of 94.7 percent, precision of 92.3 percent, recall of 96.1 percent, and an F1 score of 94.1 percent. The Random Forest model obtained an accuracy of 88.2 percent with a ROC AUC value of 0.91, indicating reliable fire risk prediction capability. Conclusion: The integrated Forest Fire Alert System outperforms conventional detection methods in terms of accuracy, detection speed, and automation. The proposed approach provides a scalable, IoT enabled, and proactive solution for intelligent wildfire monitoring and management under evolving climatic conditions.
Neuromorphic Computing Chips for Edge AI: A Comprehensive Analysis of Brain-Inspired Hardware Architecture for Real-Time Intelligent Systems Anwar Ali Sathio; Chiragh Kumar Maheshwari
International Journal of Sustainable Engineering Innovations Vol. 2 No. 1 (2026): International Journal of Sustainable Engineering Innovations (February 2026)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijsei.v2i1.151

Abstract

Background of study: Edge computing devices like autonomous robots and IoT sensors need sophisticated AI for real-time decisions, but conventional processors consume 15-300 watts during inference, creating critical limitations for battery-powered deployments. GPU-based accelerators face memory bottlenecks and high energy costs from data movement, making sustained autonomous operation impractical. Aims of paper: This research compares neuromorphic platforms (Intel Loihi 2, IBM TrueNorth, BrainChip Akida) against conventional accelerators (NVIDIA Jetson, Google Coral) to evaluate if neuromorphic architectures can solve edge AI energy efficiency challenges across five representative workloads. Methods: Using an experimental design with hardware benchmarking and power analysis, we evaluated five edge AI workloads. ANOVA and regression modeling were then applied to rigorously compare computing paradigms while controlling for variables. Result: Neuromorphic platforms demonstrated 15-50× improved energy efficiency versus conventional GPU accelerators for event-driven workloads. Intel Loihi 2 achieved 2,400 inferences/joule at 1.8 watts versus 180 inferences/joule at 18.5 watts for NVIDIA Jetson. IBM TrueNorth operated at 70 milliwatts for pattern recognition. BrainChip Akida achieved 94.6% accuracy on keyword spotting at 0.8 watts. Event-driven processing exhibited 0.4ms latency versus 5.1ms for frame-based systems. Neuromorphic chips maintained stable performance without active cooling below 65°C, while conventional accelerators required thermal management above 85°C. Conclusion: Neuromorphic processors (0.6-5W) excel in power-efficient edge AI for event-driven data. While hybrid architectures optimize performance, adoption is hindered by immature software ecosystems, limited training frameworks, and a 2-4% accuracy gap compared to conventional methods.
Forest Fire Alert System Using Satellite Imagery, Machine Learning, and GPS-Based Early Warning Mechanism Anwar Ali Sathio; Doulat Ram; Abdul Aziz Khan; Sameer Ali
International Journal of Sustainable Engineering Innovations Vol. 2 No. 1 (2026): International Journal of Sustainable Engineering Innovations (February 2026)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijsei.v2i1.152

Abstract

Background of study: Wildfires pose a critical threat to global ecosystems, biodiversity, and human safety, with climate change intensifying fire frequency, scale, and unpredictability. Traditional wildfire detection approaches often suffer from delayed response, limited coverage, and insufficient automation, which restrict effective mitigation and early intervention. Aims and scope of paper: This paper aims to design and evaluate an intelligent Forest Fire Alert System that integrates satellite remote sensing, machine learning models, Internet of Things based environmental sensing, and real time alert communication to enable early wildfire detection and proactive risk assessment. Methods: The proposed system employs a Convolutional Neural Network to detect active fire regions from multispectral satellite imagery, while a Random Forest classifier estimates wildfire risk levels based on meteorological variables and IoT sensor data. Geospatial positioning through GPS supports precise location mapping, and a web-based dashboard disseminates real time alerts to forestry authorities for rapid response. Result: Experimental evaluation demonstrates strong performance of the proposed framework. The CNN model achieved an accuracy of 94.7 percent, precision of 92.3 percent, recall of 96.1 percent, and an F1 score of 94.1 percent. The Random Forest model obtained an accuracy of 88.2 percent with a ROC AUC value of 0.91, indicating reliable fire risk prediction capability. Conclusion: The integrated Forest Fire Alert System outperforms conventional detection methods in terms of accuracy, detection speed, and automation. The proposed approach provides a scalable, IoT enabled, and proactive solution for intelligent wildfire monitoring and management under evolving climatic conditions.
Co-Authors Abdul Aziz Khan Chiragh Kumar Maheshwari Doulat Ram Sameer Ali