M
Milad Taghavi
Researcher at Cornell University
Publications - 7
Citations - 147
Milad Taghavi is an academic researcher from Cornell University. The author has contributed to research in topics: Decision tree & Cognitive radio. The author has an hindex of 5, co-authored 7 publications receiving 86 citations. Previous affiliations of Milad Taghavi include California Institute of Technology & Sharif University of Technology.
Papers
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Journal ArticleDOI
Energy-Efficient Classification for Resource-Constrained Biomedical Applications
Mahsa Shoaran,Benyamin Allahgholizadeh Haghi,Milad Taghavi,Masoud Farivar,Azita Emami-Neyestanak +4 more
TL;DR: This work proposes an efficient hardware architecture to implement gradient boosted trees in applications under stringent power, area, and delay constraints, such as medical devices, and introduces the concepts of asynchronous tree operation and sequential feature extraction to achieve an unprecedented energy and area efficiency.
Proceedings ArticleDOI
Hardware Complexity Analysis of Deep Neural Networks and Decision Tree Ensembles for Real-time Neural Data Classification
Milad Taghavi,Mahsa Shoaran +1 more
TL;DR: This analysis shows that the strict energy-area-latency trade-off can be relaxed using an ensemble of DTs, and they can be significantly more efficient than alternative DNN models, while achieving better classification accuracy in real-time neural data classification tasks.
Proceedings ArticleDOI
Cost-Efficient Classification for Neurological Disease Detection
TL;DR: A hardware-friendly machine learning model based on gradient boosted decision trees for neurological disease detection that combines fixed point quantization and cost-efficient inference to enable low-power embedded learning is proposed.
Proceedings ArticleDOI
Integrated bidirectional isolated soft-switched battery charger for vehicle-to-grid technology using 4-Switch 3Φ-rectifier
TL;DR: A new three-phase integrated bidirectional isolated soft-switched battery charger is proposed which is appropriate for vehicle to grid technology and is investigated by simulation results.
Proceedings ArticleDOI
A 41.2 nJ/class, 32-Channel On-Chip Classifier for Epileptic Seizure Detection
TL;DR: The proposed system-on-chip (SoC) breaks the strict energy-area-delay trade-off by employing area and memoryefficient techniques and achieves 27 × improvement in Energy-AreaLatency product.