PhD Researcher in Spiking Neural Networks and In-Memory Computing
6 dagen geleden
Job Overview
Objective:
The aim of this position is to investigate, design, implement, and assess a cutting-edge ultra-low-power architecture for Spiking Neural Networks (SNNs) that utilizes principles of in-memory computing to facilitate efficient online learning.
Background:
Neuromorphic computing presents a revolutionary approach to achieving intelligence at the edge by mimicking the brain's efficient biological processes through Spiking Neural Networks (SNNs). These systems not only promise significant energy savings but also improve real-time processing capabilities when combined with online learning methodologies.
Traditional von Neumann computing architectures, which separate memory and processing units, face performance limitations due to the constant data transfer between these components. This separation results in increased energy consumption and processing delays. Furthermore, the reliance on energy-demanding dynamic random-access memory (DRAM) amplifies these energy issues, particularly when addressing the computational demands of online learning tasks in SNNs. In light of these challenges, the research landscape is evolving. Innovations such as IBM's TrueNorth chip, which emulates neural networks, are gaining traction. Additionally, there is a growing interest in exploring analog, hybrid, and advanced nanoelectronic devices, particularly those with memristive characteristics. In-memory computing, which allows calculations to be performed directly within memory storage, has emerged as a preferred design approach, significantly reducing energy usage and latency.
Research Questions:
How can in-memory computing techniques be incorporated into SNN architectures to improve online learning performance? What are the trade-offs between performance, power consumption, and accuracy when implementing in-memory online learning in SNNs? How can the inherent variability and non-ideal characteristics of in-memory devices be managed or utilized in SNN-based online learning frameworks?Significance:
This research is poised to advance the field of neuromorphic engineering by integrating the advantages of SNNs with in-memory computing. The results could potentially transform ultra-low-power applications, particularly in edge devices, wearables, and IoT, thereby making intelligent systems more widespread and sustainable.
Job Requirements
Candidates should possess or anticipate obtaining a Master of Science degree or equivalent in Electrical Engineering, Applied Physics, or a related field. A solid foundation in Digital/Mixed-Signal Integrated Circuit (IC) design is essential. Proficiency in HDL (Verilog, VHDL) and scripting languages (Python, TCL) is required. Basic familiarity with commercial EDA tools (Cadence/Mentor Graphics) is preferred. Knowledge of Neuromorphic architectures and Low power IC design would be advantageous.Employment Conditions
This position offers a rewarding opportunity within a dynamic and ambitious university environment, characterized by interdisciplinary collaboration and an international network. The successful candidate will benefit from:
Full-time employment for a duration of four years, with an intermediate evaluation after nine months. Teaching responsibilities will constitute 10% of the role. Competitive salary and benefits, including a pension scheme, paid maternity and parental leave, in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,770 max. €3,539). An annual bonus of 8.3% and vacation pay of 8%. Access to high-quality training programs and support to foster development into an independent, self-aware scientific researcher. Excellent technical infrastructure, on-campus childcare, and sports facilities. Reimbursement for commuting, remote work, and internet expenses. Assistance from a Staff Immigration Team and a tax compensation scheme for international candidates.-
Eindhoven, Noord-Brabant, Nederland Eindhoven University of Technology VoltijdJob Overview Objective: The aim of this position is to investigate, design, and develop an ultra-low-power architecture for Spiking Neural Networks (SNNs) that utilizes in-memory computing techniques to facilitate efficient online learning. Background: Neuromorphic computing presents a groundbreaking approach to achieving intelligence at the edge....
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Eindhoven, Noord-Brabant, Nederland Eindhoven University of Technology VoltijdJob Overview Purpose: The objective of this position is to conduct research, design, implement, and assess a cutting-edge ultra-low-power architecture for Spiking Neural Networks (SNNs) that utilizes principles of in-memory computing to optimize online learning processes. Context: Neuromorphic computing presents a groundbreaking approach to...
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Researcher in In-Memory Computing for Spiking Neural Networks
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Eindhoven, Noord-Brabant, Nederland Eindhoven University of Technology VoltijdJob Overview Objective: The goal of this position is to investigate, design, and implement a cutting-edge Spiking Neural Network (SNN) framework that utilizes in-memory computing techniques to optimize online learning processes. Background: Neuromorphic computing presents a groundbreaking approach to achieving intelligence in edge devices. By...
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Eindhoven, Noord-Brabant, Nederland Eindhoven University of Technology VoltijdJob Overview Objective: The goal of this position is to investigate, develop, and assess a highly efficient Spiking Neural Network (SNN) framework that utilizes in-memory computing techniques to facilitate effective online learning. Background: Neuromorphic computing is emerging as a groundbreaking approach to achieving intelligence at the edge. By mimicking...
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