Tanner Andrulis
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About

I’m a Graduate Research Assistant at MIT advised by Profs. Vivienne Sze and Joel Emer in the EEMS group.

My research focuses on modeling and design of analog, compute-in-memory, and photonic deep neural network accelerators. Through cross-stack co-design, I work to develop lower-energy, higher-throughput systems.

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Publications and Talks


CiMLoop: A Flexible, Accurate, and Fast Compute-In-Memory Modeling Tool. ISPASS 2024

ISPASS 2024 Best Paper Award Winner
May 7, 2024
Tanner Andrulis, Vivienne Sze, Joel S. Emer

Architecture-Level Modeling of Photonic Deep Neural Network Accelerators. ISPASS 2024

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May 7, 2024
Tanner Andrulis, Gohar Irfan Chaudhry, Vinith M. Suriyakumar, Joel S. Emer, Vivienne Sze

Modeling Analog-Digital-Converter Energy and Area for Compute-In-Memory Accelerator Design

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Apr 9, 2024
Tanner Andrulis, Ruicong Chen, Hae-Seung Lee, Joel S. Emer, Vivienne Sze

Efficient, Accurate, and Flexible PIM Inference through Adaptable Low-Resolution Arithmetic! Master’s Thesis 2023

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Jul 1, 2023
Tanner Andrulis, Vivienne Sze, Joel S. Emer

Efficient AI Inference With Analog Processing In Memory. CSAIL Alliances Meet 2023

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Jun 27, 2023
Tanner Andrulis

RAELLA: Reforming the Arithmetic for Efficient, Low-Resolution, and Low-Loss Analog PIM: No Retraining Required! ISCA 2023

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Jun 17, 2023
Tanner Andrulis, Vivienne Sze, Joel S. Emer
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