17 citations · 27 across the 5 of their papers we have counts for
6 papers
Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures
Zirui Fu, Aleksandre Avaliani, Marco Donato
Executing machine learning inference tasks on resource-constrained edge devices requires careful hardware-software co-design optimizations. Recent examples have shown how transform…
NVMExplorer: A Framework for Cross-Stack Comparisons of Embedded Non-Volatile Memories
Lillian Pentecost, Alexander Hankin, Marco Donato +3
Repeated off-chip memory accesses to DRAM drive up operating power for data-intensive applications, and SRAM technology scaling and leakage power limits the efficiency of embedded…
Application-driven Design Exploration for Dense Ferroelectric Embedded Non-volatile Memories
Mohammad Mehdi Sharifi, Lillian Pentecost, Ramin Rajaei +8
The memory wall bottleneck is a key challenge across many data-intensive applications. Multi-level FeFET-based embedded non-volatile memories are a promising solution for denser an…
EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference
Thierry Tambe, Coleman Hooper, Lillian Pentecost +8
Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks. However, their hefty computation…
CHIPKIT: An agile, reusable open-source framework for rapid test chip development
Paul Whatmough, Marco Donato, Glenn Ko +3
The current trend for domain-specific architectures (DSAs) has led to renewed interest in research test chips to demonstrate new specialized hardware. Tape-outs also offer huge ped…
MASR: A Modular Accelerator for Sparse RNNs
Udit Gupta, Brandon Reagen, Lillian Pentecost +5
Recurrent neural networks (RNNs) are becoming the de facto solution for speech recognition. RNNs exploit long-term temporal relationships in data by applying repeated, learned tran…