78 citations · 184 across the 24 of their papers we have counts for
14 papers · 1 filter
FLINT: Efficiently Leveraging High Bandwidth Flash for Capacity-Scalable LLM Inference Acceleration
Geraldo F. Oliveira, Arash Tavakkol, Xiangyu Zhu +10
LLM inference is increasingly constrained by accelerator memory capacity rather than compute throughput. This constraint is especially acute in single-accelerator and small-node in…
At-the-Roofline Sparse Tensor Contractions on Vector Processors for Transformer Inference
Bowen Wang, Chi Zhang, Diyou Shen +3
Fine-grained weight pruning and activation sparsification have emerged as effective approaches for reducing the compute and memory cost of inference for Transformer models. In the…
RTLScout: Joint Agentic Code and Synthesis Optimization for Efficient Digital Circuits
Felix Arnold, Ryan Amaudruz, Dimitrios Tsaras +2
We present RTLScout, an autonomous system that combines LLM-driven agentic design with logic synthesis optimization and arithmetic architecture selection. An LLM agent iteratively…
FlatAttention: Dataflow and Fabric Collectives Co-Optimization for Large Attention-Based Model Inference on Tile-Based Accelerators
Chi Zhang, Luca Colagrande, Renzo Andri +1
Attention accounts for an increasingly dominant fraction of total computation during inference for mixture-of-experts (MoE) models, making efficient acceleration critical. Emerging…
GenPairX: A Hardware-Algorithm Co-Designed Accelerator for Paired-End Read Mapping
Julien Eudine, Chu Li, Zhuo Cheng +11
Genome sequencing has become a central focus in computational biology. A genome study typically begins with sequencing, which produces millions to billions of short DNA fragments k…
FlatAttention: Dataflow and Fabric Collectives Co-Optimization for Efficient Multi-Head Attention on Tile-Based Many-PE Accelerators
Chi Zhang, Luca Colagrande, Renzo Andri +6
Multi-Head Attention (MHA) is a critical computational kernel in transformer-based AI models. Emerging scalable tile-based accelerator architectures integrate increasing numbers of…
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…
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…
Flex-SFU: Accelerating DNN Activation Functions by Non-Uniform Piecewise Approximation
Enrico Reggiani, Renzo Andri, Lukas Cavigelli
Modern DNN workloads increasingly rely on activation functions consisting of computationally complex operations. This poses a challenge to current accelerators optimized for convol…
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…
A "New Ara" for Vector Computing: An Open Source Highly Efficient RISC-V V 1.0 Vector Processor Design
Matteo Perotti, Matheus Cavalcante, Nils Wistoff +3
Vector architectures are gaining traction for highly efficient processing of data-parallel workloads, driven by all major ISAs (RISC-V, Arm, Intel), and boosted by landmark chips,…
Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile
Renzo Andri, Beatrice Bussolino, Antonio Cipolletta +2
Most of today's computer vision pipelines are built around deep neural networks, where convolution operations require most of the generally high compute effort. The Winograd convol…
Identifying and Exploiting Sparse Branch Correlations for Optimizing Branch Prediction
Anastasios Zouzias, Kleovoulos Kalaitzidis, Konstantin Berestizshevsky +3
Branch prediction is arguably one of the most important speculative mechanisms within a high-performance processor architecture. A common approach to improve branch prediction accu…
YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN Acceleration
Renzo Andri, Lukas Cavigelli, Davide Rossi +1
Convolutional neural networks (CNNs) have revolutionized the world of computer vision over the last few years, pushing image classification beyond human accuracy. The computational…