32 citations · 62 across the 18 of their papers we have counts for
8 papers · 1 filter
Anda: Unlocking Efficient LLM Inference with a Variable-Length Grouped Activation Data Format
Chao Fang, Man Shi, Robin Geens +3
The widely-used, weight-only quantized large language models (LLMs), which leverage low-bit integer (INT) weights and retain floating-point (FP) activations, reduce storage require…
MATCH: Model-Aware TVM-based Compilation for Heterogeneous Edge Devices
Mohamed Amine Hamdi, Francesco Daghero, Giuseppe Maria Sarda +6
Streamlining the deployment of Deep Neural Networks (DNNs) on heterogeneous edge platforms, coupling within the same micro-controller unit (MCU) instruction processors and hardware…
Pack my weights and run! Minimizing overheads for in-memory computing accelerators
Pouya Houshmand, Marian Verhelst
In-memory computing hardware accelerators allow more than 10x improvements in peak efficiency and performance for matrix-vector multiplications (MVM) compared to conventional digit…
Optimising GPGPU Execution Through Runtime Micro-Architecture Parameter Analysis
Giuseppe M. Sarda, Nimish Shah, Debjyoti Bhattacharjee +2
GPGPU execution analysis has always been tied to closed-source, proprietary benchmarking tools that provide high-level, non-exhaustive, and/or statistical information, preventing a…
Optimizing Layer-Fused Scheduling of Transformer Networks on Multi-accelerator Platforms
Steven Colleman, Arne Symons, Victor J. B. Jung +1
The impact of transformer networks is booming, yet, they come with significant computational complexity. It is therefore essential to understand how to optimally map and execute th…
HTVM: Efficient Neural Network Deployment On Heterogeneous TinyML Platforms
Josse Van Delm, Maarten Vandersteegen, Alessio Burrello +5
Optimal deployment of deep neural networks (DNNs) on state-of-the-art Systems-on-Chips (SoCs) is crucial for tiny machine learning (TinyML) at the edge. The complexity of these SoC…
ACCO: Automated Causal CNN Scheduling Optimizer for Real-Time Edge Accelerators
Jun Yin, Linyan Mei, Andre Guntoro +1
Spatio-Temporal Convolutional Neural Networks (ST-CNN) allow extending CNN capabilities from image processing to consecutive temporal-pattern recognition. Generally, state-of-the-a…
Analog or Digital In-memory Computing? Benchmarking through Quantitative Modeling
Jiacong Sun, Pouya Houshmand, Marian Verhelst
In-Memory Computing (IMC) has emerged as a promising paradigm for energy-efficient, throughput-efficient and area-efficient machine learning at the edge. However, the differences i…