595 citations · 1.9k across the 91 of their papers we have counts for
6 papers · 1 filter
MLPerf Mobile Inference Benchmark
Vijay Janapa Reddi, David Kanter, Peter Mattson +24
This paper presents the first industry-standard open-source machine learning (ML) benchmark to allow perfor mance and accuracy evaluation of mobile devices with different AI chips…
TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems
Robert David, Jared Duke, Advait Jain +10
Deep learning inference on embedded devices is a burgeoning field with myriad applications because tiny embedded devices are omnipresent. But we must overcome major challenges befo…
AI Tax: The Hidden Cost of AI Data Center Applications
Daniel Richins, Dharmisha Doshi, Matthew Blackmore +9
Artificial intelligence and machine learning are experiencing widespread adoption in industry and academia. This has been driven by rapid advances in the applications and accuracy…
Exceeding Conservative Limits: A Consolidated Analysis on Modern Hardware Margins
George Papadimitriou, Athanasios Chatzidimitriou, Dimitris Gizopoulos +5
Modern large-scale computing systems (data centers, supercomputers, cloud and edge setups and high-end cyber-physical systems) employ heterogeneous architectures that consist of mu…
Benchmarking TinyML Systems: Challenges and Direction
Colby R. Banbury, Vijay Janapa Reddi, Max Lam +14
Recent advancements in ultra-low-power machine learning (TinyML) hardware promises to unlock an entirely new class of smart applications. However, continued progress is limited by…
Quantized Neural Network Inference with Precision Batching
Maximilian Lam, Zachary Yedidia, Colby Banbury +1
We present PrecisionBatching, a quantized inference algorithm for speeding up neural network execution on traditional hardware platforms at low bitwidths without the need for retra…