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20192026
most citedDeep-PowerX: A Deep Learning-Based Framework for Low-Power Approximate Logic Synthesis

17 citations · 42 across the 45 of their papers we have counts for

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10 papers · 1 filter

cs.LG2026

Multi-Level Modeling of Large Language Model Inference Latency and Energy via Hybrid Analytical--Machine-Learning Predictors

Saeid Shokoufa, Mohammad Erfan Sadeghi, Mehdi Kamal +1

The rapid scaling of Large Language Models (LLMs) has significantly increased computational cost, energy consumption, and inference latency, making accurate estimation essential fo…

cs.LG2025

MARCO: Hardware-Aware Neural Architecture Search for Edge Devices with Multi-Agent Reinforcement Learning and Conformal Prediction Filtering

Arya Fayyazi, Mehdi Kamal, Massoud Pedram

This paper introduces MARCO (Multi-Agent Reinforcement learning with Conformal Optimization), a novel hardware-aware framework for efficient neural architecture search (NAS) target…

cs.LG2025

RocketPPA: Code-Level Power, Performance, and Area Prediction via LLM and Mixture of Experts

Armin Abdollahi, Mehdi Kamal, Massoud Pedram

This paper presents RocketPPA, a novel ultra-fast power, performance (delay), and area (PPA) estimator operating directly at the code-level abstraction using HDL code as input. The…

cs.LG2024

Efficient Noise Mitigation for Enhancing Inference Accuracy in DNNs on Mixed-Signal Accelerators

Seyedarmin Azizi, Mohammad Erfan Sadeghi, Mehdi Kamal +1

In this paper, we propose a framework to enhance the robustness of the neural models by mitigating the effects of process-induced and aging-related variations of analog computing c…

cs.LG2024

Dynamic Co-Optimization Compiler: Leveraging Multi-Agent Reinforcement Learning for Enhanced DNN Accelerator Performance

Arya Fayyazi, Mehdi Kamal, Massoud Pedram

This paper introduces a novel Dynamic Co-Optimization Compiler (DCOC), which employs an adaptive Multi-Agent Reinforcement Learning (MARL) framework to enhance the efficiency of ma…

cs.LG2023

Low-Precision Mixed-Computation Models for Inference on Edge

Seyedarmin Azizi, Mahdi Nazemi, Mehdi Kamal +1

This paper presents a mixed-computation neural network processing approach for edge applications that incorporates low-precision (low-width) Posit and low-precision fixed point (Fi…