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Gonugondla, Sujan
23  results:
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1

Deep In-memory Architectures for Machine Learning:

Kang, Mingu ; Gonugondla, Sujan ; Shanbhag, Naresh R - 1st ed. 2020 . , 2020
Link: https://doi.org/10.1007/..
 
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2

PROMISE: A DIMA-Based Accelerator:

, In: Deep In-memory Architectures for Machine Learning,
Kang, Mingu ; Gonugondla, Sujan ; Shanbhag, Naresh R. - p. 139-160 , 2020
Link: https://doi.org/10.1007/..
 
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3

The Deep In-Memory Architecture (DIMA):

, In: Deep In-memory Architectures for Machine Learning,
Kang, Mingu ; Gonugondla, Sujan ; Shanbhag, Naresh R. - p. 7-47 , 2020
Link: https://doi.org/10.1007/..
 
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4

Introduction:

, In: Deep In-memory Architectures for Machine Learning,
Kang, Mingu ; Gonugondla, Sujan ; Shanbhag, Naresh R. - p. 1-6 , 2020
Link: https://doi.org/10.1007/..
 
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5

Correction to: Deep In-memory Architectures for Machine Lea..:

, In: Deep In-memory Architectures for Machine Learning,
Kang, Mingu ; Gonugondla, Sujan ; Shanbhag, Naresh R. - p. C1-C1 , 2020
Link: https://doi.org/10.1007/..
 
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6

A Variation-Tolerant DIMA via On-Chip Training:

, In: Deep In-memory Architectures for Machine Learning,
Kang, Mingu ; Gonugondla, Sujan ; Shanbhag, Naresh R. - p. 81-100 , 2020
Link: https://doi.org/10.1007/..
 
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7

Future Prospects:

, In: Deep In-memory Architectures for Machine Learning,
Kang, Mingu ; Gonugondla, Sujan ; Shanbhag, Naresh R. - p. 161-162 , 2020
Link: https://doi.org/10.1007/..
 
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8

Mapping Inference Algorithms to DIMA:

, In: Deep In-memory Architectures for Machine Learning,
Kang, Mingu ; Gonugondla, Sujan ; Shanbhag, Naresh R. - p. 101-137 , 2020
Link: https://doi.org/10.1007/..
 
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9

DIMA Prototype Integrated Circuits:

, In: Deep In-memory Architectures for Machine Learning,
Kang, Mingu ; Gonugondla, Sujan ; Shanbhag, Naresh R. - p. 49-79 , 2020
Link: https://doi.org/10.1007/..
 
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10

PRIVE: Efficient RRAM Programming with Chip Verification fo..:

, In: 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE),
He, Wangxin ; Meng, Jian ; Gonugondla, Sujan Kumar... - p. 1-6 , 2023
Link: https://doi.org/10.23919..
 
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11

Towards Greener Yet Powerful Code Generation via Quantizati..:

, In: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering,
Wei, Xiaokai ; Gonugondla, Sujan Kumar ; Wang, Shiqi... - p. 224-236 , 2023
Link: https://dl.acm.org/doi/1..
 
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12

Fundamental Limits on Energy-Delay-Accuracy of In-Memory Ar..:

Gonugondla, Sujan K. ; Sakr, Charbel ; Dbouk, Hassan.
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.  41 (2022)  10 - p. 3188-3201 , 2022
Link: https://doi.org/10.1109/..
 
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13

IMPQ: Reduced Complexity Neural Networks Via Granular Preci..:

, In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
Gonugondla, Sujan Kumar ; Shanbhag, Naresh R. - p. 66-70 , 2022
Link: https://doi.org/10.1109/..
 
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14

Fundamental limits on the precision of in-memory architectu..:

, In: Proceedings of the 39th International Conference on Computer-Aided Design,
Gonugondla, Sujan K. ; Sakr, Charbel ; Dbouk, Hassan. - p. 1-9 , 2020
Link: https://dl.acm.org/doi/1..
 
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15

KeyRAM: A 0.34 uJ/decision 18 k decisions/s Recurrent Atten..:

, In: 2020 IEEE Custom Integrated Circuits Conference (CICC),
Dbouk, Hassan ; Gonugondla, Sujan K. ; Sakr, Charbel. - p. 1-4 , 2020
Link: https://doi.org/10.1109/..
 
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