Objective:

To research, design, implement, and evaluate an ultra-low-power Spiking Neural Network (SNN) architecture that leverages in-memory computing principles for efficient online learning.

Background:

 The field of neuromorphic computing seems to offer a transformative solution for achieving intelligence at the edge. By emulating the brain's efficient biological mechanisms through Spiking Neural Networks (SNNs), neuromorphic computing systems not only promise substantial energy efficiency but also enhance real-time processing capabilities when integrated with online learning.

The conventional von Neumann computing architectures, characterized by separate memory and processing units, encounter performance constraints due to the continual data transfer between these segments. This structure leads to heightened energy consumption and processing time. Additionally, the widespread reliance on energy-intensive dynamic random-access memory (DRAM) exacerbates these energy concerns, particularly when grappling with the intensive computational requirements of online learning tasks in SNNs. In response to these challenges, the research landscape is shifting. Notable innovations like IBM's TrueNorth chip, which mirrors neural networks, are emerging. Alongside these digital solutions, there's a burgeoning interest in exploring analog, hybrid, and advanced nanoelectronic devices, with a keen focus on those boasting memristive attributes. In-memory computing, which conducts calculations directly within memory storage, has become a popular design choice, further reducing energy while decreasing latency.

Research Questions:

1. How can in-memory computing principles be integrated into SNN architectures to enhance online learning capabilities?
2. What are the trade-offs between performance, power, and accuracy when implementing in-memory online learning in SNNs?
3. How can the inherent variability and non-ideality of in-memory devices be mitigated or exploited in SNN-based online learning systems?

Significance:

This research aims to push the boundaries of neuromorphic engineering by combining the strengths of SNNs and in-memory computing. The outcome has the potential to revolutionize ultra-low-power applications, especially in edge devices, wearables, and IoT, making intelligent systems more pervasive and sustainable.

• Applicants must have or expect to receive a Master of Science degree or equivalent in Electrical Engineering, Applied Physics, or a related discipline.
• Strong background in Digital/Mixed-Signal Integrated Circuit (IC) design.
• Very good skills in HDL (Verilog, VHDL) and scripting languages (Python, TCL).
• Basic knowledge on commercial EDA tools (Cadence/Mentor Graphics).
• Knowledge in Neuromorphic architectures and Low power IC design would be a definite plus.

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,872 max. €3,670).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.