This work will present an embedded neuromorphic platform developed for long-term unintended gamma radiation monitoring. We describe the hardware architecture and supporting software developed to demonstrate neuromorphic computing for applications where ...

Network architecture search (NAS) is a tedious process and hence a different approach to train a large over parameterized network, followed by a progressively shrinking algorithm towards targeting the best efficient models for hardware platforms is ...

In recent years, Compute-in-memory (CiM) architectures have emerged as a promising solution for deep neural network (NN) accelerators. Multiply-accumulate (MAC) is considered a de facto unit operation in NNs. By leveraging the minimal data movement ...

CNN is indispensable for today’s Artificial Intelligence (AI) applications, but brings dominantly large overhead of data communication. Current works mainly focus on prior off-chip or intuitive/heuristic on-chip access optimization, but with the ...

Quantum computing holds transformative potential for various fields, yet its practical application is hindered by the susceptibility to errors. This study makes a pioneering contribution by applying quantum error correction codes (QECCs) for complex, ...

Reliable and low-power stress-detector ‘at the edge’ is extremely beneficial for continuously monitoring hospitalized patients. In this context, feed-forward spiking neural networks (SNNs) for stress-detection using physiological time-series signals of ...

With the increasing complexity of applications and the improvement of computational power, modern neural networks (DNNs) have become more memory-intensive. To address the bandwidth problem, modern hardware architectures often incorporate multi-level ...