Institut für Mikroelektronik

M.Sc. Joschua Conrad

Joschua Conrad received the B.Eng. degree at Baden-Wuerttemberg Cooperative State University (DHBW) Stuttgart in 2016. In his studies and work at Eisenmann SE in Böblingen and Stuttgart, he designed object-orientated software for applications with real-time requirements in automation. During his master studies, he worked at the Institute of Microelectronics at Ulm University and developed a high SNDR filter and a high SNDR oscillator using PCBs and developed software solutions for requirements engineering at Gigatronik GmbH in Ulm. He finished his master thesis in 2019 at the Institute of Microelectronics with the topic "Design of a Ring Amplifier based Sigma Delta Modulator".
He now works under the supervision of Prof Dr.-Ing. Maurits Ortmanns in the fileld of mixed signal neural network processors.

Projekte

Leistungseffiziente Deep Neural Networks basierend auf der Co-Optimierung mit Mixed-Signal Integrierten Schaltungen

J. Conrad: EdgeAI ist das verteilte Computing-Paradigma für die Ausführung von Machine-Learning-Algorithmen in der Nähe des Sensors. Im Vergleich zu zentralisierten, z. B. Cloud-basierten Lösungen werden Datensicherheit, niedrige Latenzzeiten und geringere Bandbreiten erreicht. Gleichzeitig besteht das große Problem, dass der Stromverbrauch der heutigen tiefen neuronalen Netze (die häufigste Art von Machine-Learning-Algorithmen) für solche Anwendungen viel zu hoch ist. ... [mehr]

Studentische Arbeiten

[mt] = Masterarbeit, [rp] = Bachelorarbeit

Aktuelle Arbeiten

  • Johannes Stark
    Implementation of an In-Memory-Compute Circuit for the Inference of Neural Networks [mt]
  • Nour Elshahawy
    Evaluation of Methods for Benchmarking and Re-Using SRAM Memory [mt]

Abgeschlossene Arbeiten

  • Kilian Storch
    Evaluation of DRAM Links for Neural-Network Inference-Accelerators [mt]
  • Simon Wilhelmstätter
    Design and Implementation of the Dataflow for a Versatile Neural-Network Inference-System [mt]
  • Simone Steinhauser
    Investigation of the Data-Flow in a Neural-Netwok Inference System [rp]
  • Rawan Hagag
    Investigation and Design of Comparator Architectures for a SAR ADC in 28nm CMOS [rp]
  • Luca Krüger
    Analyzing the Influence of Neural-Network Hyperparameters on the Resilience over Mixed-Signal Hardware Errors [rp]
  • Biyi Jiang
    Modeling of Neural-Network Processing-Element Hardware on Algorithmic Level [mt]
  • Paul Kässer
    Development and Test of a Mixed-Signal Neural-Network Processing-Element [mt]
  • Franjo Lovric
    Evaluation of System-Level Structures for Neural-Network Accelerator Systems [mt]

Publikationen

2024

9.
Conrad, J.; Wilhelmstätter, S.; Asthana, R.; Belagiannis, V.; Ortmanns, M.
Differentiable Cost Model for Neural-Network Accelerator Regarding Memory Hierarchy
IEEE Transactions on Circuits and Systems I: Regular Papers ( Early Access )
Oktober 2024
DOI:10.1109/TCSI.2024.3476534
8.
Conrad, J.; Kauffman, J. G.; Wilhelmstätter, S.; Asthana, R.; Belagiannis, V.; Ortmanns, M.
Confidence Estimation and Boosting for Dynamic-Comparator Transient-Noise Analysis
22nd IEEE Interregional NEWCAS Conference (NEWCAS)
September 2024
DOI:10.1109/NewCAS58973.2024.10666354
7.
Wilhelmstätter, S.; Conrad, J.; Upadhyaya, D.; Polian, I.; Ortmanns, M.
Enabling Power Side-Channel Attack Simulation on Mixed-Signal Neural Network Accelerators
IEEE International Conference on Omni-Layer Intelligent Systems (COINS), London, UK
Juli 2024
6.
Kässer, P.; Kaltenstadler, S.; Conrad, J.; Wagner, J.; Ismail, O.; Ortmanns, M.
Stability Prediction of Δ∑ Modulators using Artificial Neural Networks
IEEE International Symposium on Circuits and Systems (ISCAS), Singapore
Mai 2024
DOI:10.1109/ISCAS58744.2024.10557868
5.
Conrad, J.; Wilhelmstätter, S.; Asthana, R.; Belagiannis, V.; Ortmanns, M.
Too-Hot-to-Handle: Insights into Temperature and Noise Hyperparameters for Differentiable Neural-Architecture-Searches
6th IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Abu-Dhabi, UAE
April 2024
DOI:10.1109/AICAS59952.2024.10595971
4.
Wilhelmstätter, S.; Conrad, J.; Upadhyaya, D.; Polian, I.; Ortmanns, M.
Attacking a Joint Protection Scheme for Deep Neural Network Hardware Accelerators and Models
6th IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Abu Dhabi, UAE
April 2024
DOI:10.1109/AICAS59952.2024.10595935
3.
Asthana, R.; Conrad, J.; Dawoud, Y.; Ortmanns, M.; Belagiannis, V.
Multi-conditioned Graph Diffusion for Neural Architecture Search
Transactions on Machine Learning Research
März 2024
ISSN: 2835-8856
Weblink:https://openreview.net/forum?id=5VotySkajV

2021

2.
Conrad, J.; Jiang, B.; Kässer, P.; Belagiannis, V.; Ortmanns, M.
Nonlinearity Modeling for Mixed-Signal Inference Accelerators in Training Frameworks
28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS), pp. 1-4
2021
DOI:10.1109/ICECS53924.2021.9665503

2020

1.
Conrad, J.; Vogelmann, P.; Mokhtar, M. A.; Ortmanns, M.
Design Approach for Ring Amplifiers
IEEE Transactions on Circuits and Systems I: Regular Papers
April 2020
DOI:10.1109/TCSI.2020.2986553

Wissenschaftl. Mitarbeiter

Joschua Conrad