activity
20222025
most citedA "New Ara" for Vector Computing: An Open Source Highly Efficient RISC-V V 1.0 Vector Processor Design

78 citations · 186 across the 17 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.AR2023

Near-Memory Parallel Indexing and Coalescing: Enabling Highly Efficient Indirect Access for SpMV

Chi Zhang, Paul Scheffler, Thomas Benz +2

Sparse matrix vector multiplication (SpMV) is central to numerous data-intensive applications, but requires streaming indirect memory accesses that severely degrade both processing…

cs.AR2023★ 1 cited

Stella Nera: A Differentiable Maddness-Based Hardware Accelerator for Efficient Approximate Matrix Multiplication

Jannis Schönleber, Lukas Cavigelli, Matteo Perotti +2

Artificial intelligence has surged in recent years, with advancements in machine learning rapidly impacting nearly every area of life. However, the growing complexity of these mode…

cs.AR2023★ 34 cited

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…

cs.AR2023★ 2 cited

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…

cs.AR2023★ 34 cited

DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training

Angelo Garofalo, Yvan Tortorella, Matteo Perotti +5

On-chip DNN inference and training at the Extreme-Edge (TinyML) impose strict latency, throughput, accuracy and flexibility requirements. Heterogeneous clusters are promising solut…

cs.AR2023

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…