78 citations · 225 across the 19 of their papers we have counts for
6 papers · 1 filter
Ara2: Exploring Single- and Multi-Core Vector Processing with an Efficient RVV 1.0 Compliant Open-Source Processor
Matteo Perotti, Matheus Cavalcante, Renzo Andri +2
Vector processing is highly effective in boosting processor performance and efficiency for data-parallel workloads. In this paper, we present Ara2, the first fully open-source vect…
Spatz: Clustering Compact RISC-V-Based Vector Units to Maximize Computing Efficiency
Matteo Perotti, Samuel Riedel, Matheus Cavalcante +1
The ever-increasing computational and storage requirements of modern applications and the slowdown of technology scaling pose major challenges to designing and implementing efficie…
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…
FlooNoC: A Multi-Tbps Wide NoC for Heterogeneous AXI4 Traffic
Tim Fischer, Michael Rogenmoser, Matheus Cavalcante +2
Meeting the staggering bandwidth requirements of today's applications challenges the traditional narrow and serialized NoCs, which hit hard bounds on the maximum operating frequenc…
MemPool: A Scalable Manycore Architecture with a Low-Latency Shared L1 Memory
Samuel Riedel, Matheus Cavalcante, Renzo Andri +1
Shared L1 memory clusters are a common architectural pattern (e.g., in GPGPUs) for building efficient and flexible multi-processing-element (PE) engines. However, it is a common be…
Quark: An Integer RISC-V Vector Processor for Sub-Byte Quantized DNN Inference
MohammadHossein AskariHemmat, Theo Dupuis, Yoan Fournier +8
In this paper, we present Quark, an integer RISC-V vector processor specifically tailored for sub-byte DNN inference. Quark is implemented in GlobalFoundries' 22FDX FD-SOI technolo…