Daniel Bankman

661 citations

13 papers · 437 indexed · h-index 10

Electrical and Electronic Engineering top 10%
Artificial Intelligence top 10%
Computer Vision and Pattern Recognition top 10%
Biomedical Engineering
Cellular and Molecular Neuroscience

Co-authors: Boris Murmann Bert Moons Marian Verhelst Lita Yang Daisuke Miyashita Andrew Yu Jakob W. Messner Subhasish Mitra
Topics: Advanced Memory and Neural Computing (7 papers)Analog and Mixed-Signal Circuit Design (5 papers)Advanced Neural Network Applications (5 papers)
Cited by: Electrical and Electronic Engineering Computer Vision and Pattern Recognition Hardware and Architecture
Journals: IEEE Journal of Solid-State Circuits Electronics Letters IEEE Solid-State Circuits Magazine
Partner nations: United States Belgium

In The Last Decade

Daniel Bankman

13 papers receiving 425 citations

Peers

Countries citing papers authored by Daniel Bankman

Since Specialization

Citations

This map shows the geographic impact of Daniel Bankman's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Bankman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Bankman more than expected).

Fields of papers citing papers by Daniel Bankman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Bankman. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Bankman. The network helps show where Daniel Bankman may publish in the future.

Co-authorship network of co-authors of Daniel Bankman

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Bankman. A scholar is included among the top collaborators of Daniel Bankman based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel Bankman. Daniel Bankman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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13 of 13 papers shown

#	Work	Indexed citations
1	RRAM-Based In-Memory Computing for Embedded Deep Neural Networks 2019 ·Daniel Bankman,Jakob W. Messner,(unknown),Boris Murmann	8
2	Understanding Metastability in SAR ADCs: Part II: Asynchronous 2019 ·IEEE Solid-State Circuits Magazine ·Andrew Yu,Daniel Bankman,(unknown),Boris Murmann	15
3	Understanding Metastability in SAR ADCs: Part I: Synchronous 2019 ·IEEE Solid-State Circuits Magazine ·Daniel Bankman,Andrew Yu,(unknown),Boris Murmann	9
4	An always-on 3.8μJ/86% CIFAR-10 mixed-signal binary CNN processor with all memory on chip in 28nm CMOS 2018 ·Daniel Bankman,Lita Yang,Bert Moons,Marian Verhelst,Boris Murmann	116
5	TRIG 2018 ·Gage Hills,Daniel Bankman,Bert Moons,Lita Yang,(unknown),(unknown),Rebecca Park,Marian Verhelst,Boris Murmann,Max M. Shulaker,H.‐S. Philip Wong,Subhasish Mitra	4
6	An Always-On 3.8 <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math> </inline-formula>J/86% CIFAR-10 Mixed-Signal Binary CNN Processor With All Memory on Chip in 28-nm CMOS 2018 ·IEEE Journal of Solid-State Circuits ·Daniel Bankman,Lita Yang,Bert Moons,Marian Verhelst,Boris Murmann	131
7	Embedded Deep Learning: Algorithms, Architectures and Circuits for Always-on Neural Network Processing 2018 ·CERN Document Server (European Organization for Nuclear Research) ·Bert Moons,Daniel Bankman,Marian Verhelst	9
8	Embedded Deep Learning 2018 ·Bert Moons,Daniel Bankman,Marian Verhelst	33
9	TRIG: Hardware Accelerator for Inference-Based Applications and Experimental Demonstration Using Carbon Nanotube FETs 2018 ·Gage Hills,Daniel Bankman,Bert Moons,Lita Yang,(unknown),(unknown),(unknown),Marian Verhelst,Boris Murmann,Max M. Shulaker,H.‐S. Philip Wong,Subhasish Mitra	4
10	Bit Error Tolerance of a CIFAR-10 Binarized Convolutional Neural Network Processor 2018 ·Lita Yang,Daniel Bankman,Bert Moons,Marian Verhelst,Boris Murmann	23
11	An 8-bit, 16 input, 3.2 pJ/op switched-capacitor dot product circuit in 28-nm FDSOI CMOS 2016 ·Daniel Bankman,Boris Murmann	35
12	Mixed-signal circuits for embedded machine-learning applications 2015 ·Boris Murmann,Daniel Bankman,(unknown),Daisuke Miyashita,Lita Yang	38
13	Passive charge redistribution digital‐to‐analogue multiplier 2015 ·Electronics Letters ·Daniel Bankman,Boris Murmann	12

About Daniel Bankman

Daniel Bankman is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Biomedical Engineering, having authored 13 papers that have together received 437 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (7 papers), Analog and Mixed-Signal Circuit Design (5 papers) and Advanced Neural Network Applications (5 papers). The work is most often cited by research in Electrical and Electronic Engineering (361 citations), Computer Vision and Pattern Recognition (102 citations) and Hardware and Architecture (33 citations). Daniel Bankman has collaborated with scholars based in United States and Belgium. Frequent co-authors include Boris Murmann, Bert Moons, Marian Verhelst, Lita Yang, Daisuke Miyashita, Andrew Yu, Jakob W. Messner, Subhasish Mitra, Gage Hills and Max M. Shulaker. Their work appears in journals such as IEEE Journal of Solid-State Circuits, Electronics Letters and IEEE Solid-State Circuits Magazine.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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