5 citations · 11 across the 7 of their papers we have counts for
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QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration
Ahmet Inci, Siri Garudanagiri Virupaksha, Aman Jain +4
As the machine learning and systems communities strive to achieve higher energy-efficiency through custom deep neural network (DNN) accelerators, varied precision or quantization l…
Efficient Deep Learning Using Non-Volatile Memory Technology
Ahmet Inci, Mehmet Meric Isgenc, Diana Marculescu
Embedded machine learning (ML) systems have now become the dominant platform for deploying ML serving tasks and are projected to become of equal importance for training ML models.…
QADAM: Quantization-Aware DNN Accelerator Modeling for Pareto-Optimality
Ahmet Inci, Siri Garudanagiri Virupaksha, Aman Jain +3
As the machine learning and systems communities strive to achieve higher energy-efficiency through custom deep neural network (DNN) accelerators, varied bit precision or quantizati…
QAPPA: Quantization-Aware Power, Performance, and Area Modeling of DNN Accelerators
Ahmet Inci, Siri Garudanagiri Virupaksha, Aman Jain +3
As the machine learning and systems community strives to achieve higher energy-efficiency through custom DNN accelerators and model compression techniques, there is a need for a de…
DeepNVM++: Cross-Layer Modeling and Optimization Framework of Non-Volatile Memories for Deep Learning
Ahmet Inci, Mehmet Meric Isgenc, Diana Marculescu
Non-volatile memory (NVM) technologies such as spin-transfer torque magnetic random access memory (STT-MRAM) and spin-orbit torque magnetic random access memory (SOT-MRAM) have sig…