EASTER: Learning to Split Transformers at the Edge Robustly
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HMPSoCs combine diferent processors on a single chip. They enable powerful embedded devices, which increasingly perform ML inference tasks at the edge. State-of-the-art HMPSoCs can …
Edge artificial intelligence (AI) is an innovative computing paradigm that aims to shift the training and inference of machine learning models to the edge of the network. This …
3D-stacked processor-memory systems stack memory (DRAM banks) directly on top of logic (CPU cores) using chiplet-on-chiplet packaging technology to provide the next-level computing …
For model inference of Convolutional Neural Networks (CNNs), we nowadays witness a shift from the Cloud to the Edge. Unfortunately, deploying and inferring large, compute- and …
Convolutional Neural Networks (CNNs) are biologically inspired computational models that are at the heart of many modern computer vision and natural language processing …
The automated architecture search methodology for neural networks is known as Neural Architecture Search (NAS). In recent times, Convolutional Neural Networks (CNNs) designed …
Shared caches in multicore processors introduce serious difficulties in providing guarantees on the real-time properties of embedded software due to the interaction and the …
As embedded systems grow more complex and as new applications such as IoT require many design constraints, sophisticated design space explo- ration techniques are essential in …
The application workloads in modern MPSoC-based embedded systems are becoming increasingly dynamic. Different applications concurrently execute and contend for resources in such …