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EDBT 2026 Demo / reviewers in the wild / expert

Sonu Prasad

dblp:331/1704 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 56% Hardware accelerators and domain-specific architectures · 44%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Hardware accelerators and domain-specific architectures › edge intelligence
edge and embedded machine learning		 0.612022
TMM-TinyML: tensor memory mapping (TMM) method for tiny machine learning (TinyML) · MobiCom 2022
Embedded and real-time systems › embedded machine learning
TinyML		 0.612022
TMM-TinyML: tensor memory mapping (TMM) method for tiny machine learning (TinyML) · MobiCom 2022
Embedded and real-time systems › embedded processor
microcontroller		 0.212022
TMM-TinyML: tensor memory mapping (TMM) method for tiny machine learning (TinyML) · MobiCom 2022

Methods — techniques the papers use, named apart from their topics

tensor memory mapping · 0.6
YearPublicationVenuePosition
2022 TMM-TinyML: tensor memory mapping (TMM) method for tiny machine learning (TinyML)
abstract
TinyML: Tiny in size, big in impact! In this paper, we present a Tensor Memory Mapping (TMM) method, which can accurately calculate the on-device execution memory consumed by a range of ML and TinyML models during execution on small central processing units (CPUs), microcontroller units (MCUs), and single board computers (SBCs).
Bharath Sudharsan, Sonu Prasad, Dan Jose, John G. Breslin
 MobiCom2