428 citations · 4.5k across the 191 of their papers we have counts for
66 papers · 1 filter
Scalable Hierarchical Instruction Cache for Ultra-Low-Power Processors Clusters
Jie Chen, Igor Loi, Eric Flamand +3
High Performance and Energy Efficiency are critical requirements for Internet of Things (IoT) end-nodes. Exploiting tightly-coupled clusters of programmable processors (CMPs) has r…
PATRONoC: Parallel AXI Transport Reducing Overhead for Networks-on-Chip targeting Multi-Accelerator DNN Platforms at the Edge
Vikram Jain, Matheus Cavalcante, Nazareno Bruschi +6
Emerging deep neural network (DNN) applications require high-performance multi-core hardware acceleration with large data bursts. Classical network-on-chips (NoCs) use serial packe…
ITA: An Energy-Efficient Attention and Softmax Accelerator for Quantized Transformers
Gamze İslamoğlu, Moritz Scherer, Gianna Paulin +4
Transformer networks have emerged as the state-of-the-art approach for natural language processing tasks and are gaining popularity in other domains such as computer vision and aud…
Towards a RISC-V Open Platform for Next-generation Automotive ECUs
Luca Cuomo, Claudio Scordino, Alessandro Ottaviano +5
The complexity of automotive systems is increasing quickly due to the integration of novel functionalities such as assisted or autonomous driving. However, increasing complexity po…
A 3 TOPS/W RISC-V Parallel Cluster for Inference of Fine-Grain Mixed-Precision Quantized Neural Networks
Alessandro Nadalini, Georg Rutishauser, Alessio Burrello +5
The emerging trend of deploying complex algorithms, such as Deep Neural Networks (DNNs), increasingly poses strict memory and energy efficiency requirements on Internet-of-Things (…
A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms
Cristina Silvano, Daniele Ielmini, Fabrizio Ferrandi +19
Recent trends in deep learning (DL) have made hardware accelerators essential for various high-performance computing (HPC) applications, including image classification, computer vi…