Engin İpek

Micron (United States) — United States

Computer Science · Physical Sciences

31h-index7.2kcitations84works0.42yr avg
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Research Topics

Parallel Computing and Optimization Techniques(54), Advanced Memory and Neural Computing(31), Advanced Data Storage Technologies(25), Ferroelectric and Negative Capacitance Devices(15), Low-power high-performance VLSI design(14)

Publications84 total

ANVIL: An In-Storage Accelerator for Name–Value Data Stores
2025· 1 citedOpen Access
TCAM-SSD: A Framework for Search-Based Computing in Solid-State Drives
arXiv (Cornell University)·2024Open Access
Industry Track Program Committee
2024Open Access
TCAM-SSD: A Framework for Search-Based Computing in Solid-State Drives
2024· 1 citedOpen Access
TCAM-SSD: A Framework for Search-Based Computing in Solid-State Drives
2024
An Analog Preconditioner for Solving Linear Systems [Slides]
2021Open Access
An Analog Preconditioner for Solving Linear Systems [Slides]
2021Open Access
An Analog Preconditioner for Solving Linear Systems
2021· 28 citedOpen Access
Adapting In Situ Accelerators for Sparsity with Granular Matrix Reordering
IEEE Computer Architecture Letters·2020· 1 citedOpen Access
Commutative Data Reordering: A New Technique to Reduce Data Movement Energy on Sparse Inference Workloads
2020· 5 citedOpen Access
Memristive Accelerators for Dense and Sparse Linear Algebra: From Machine Learning to High-Performance Scientific Computing
IEEE Micro·2019· 6 citedOpen Access
Content Aware Refresh: Exploiting the Asymmetry of DRAM Retention Errors to Reduce the Refresh Frequency of Less Vulnerable Data
IEEE Transactions on Computers·2018· 15 cited
Enabling Scientific Computing on Memristive Accelerators
2018· 103 cited
Vertical Writes: Closing the Throughput Gap between Deeply Scaled STT-MRAM and DRAM
IEEE Computer Architecture Letters·2018
Bit-Level Load Balancing: A New Technique for Improving the Write Throughput of Deeply Scaled STT-MRAM
IEEE Computer Architecture Letters·2018· 1 cited
Program Committee
2018Open Access
Making Memristive Neural Network Accelerators Reliable
2018· 132 cited
Multiscale Co-Design Analysis of Energy, Latency, Area, and Accuracy of a ReRAM Analog Neural Training Accelerator
IEEE Journal on Emerging and Selected Topics in Circuits and Systems·2018· 177 citedOpen Access
Sanitizer: Mitigating the Impact of Expensive ECC Checks on STT-MRAM Based Main Memories
IEEE Transactions on Computers·2017· 26 cited
Memristive Boltzmann machine: A hardware accelerator for combinatorial optimization and deep learning
2017· 7 cited
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Frequent Co-authors

Eby G. Friedman(10), Mahdi Nazm Bojnordi(9), Xiaochen Guo(9), Doug Burger(8), Rich Caruana(8), José F. Martínez(7), Sally A. McKee(7), Bronis R. de Supinski(7), Martin Schulz(7), Onur Mutlu(6), Ravi B. Patel(5), Yuxin Bai(5), Benjamin C. Lee(4), Jeremy Condit(4), Edmund B. Nightingale(4), Thomas Moscibroda(3), Ben Feinberg(3), Qing Guo(3), Qing Guo(3), Mohammad Kazemi(3)