22 citations · 87 across the 18 of their papers we have counts for
5 papers · 1 filter
Energy-efficient Machine Learning in Silicon: A Communications-inspired Approach
Naresh R. Shanbhag
This position paper advocates a communications-inspired approach to the design of machine learning systems on energy-constrained embedded `always-on' platforms. The communications-…
A 481pJ/decision 3.4M decision/s Multifunctional Deep In-memory Inference Processor using Standard 6T SRAM Array
Mingu Kang, Sujan Gonugondla, Ameya Patil +1
This paper describes a multi-functional deep in-memory processor for inference applications. Deep in-memory processing is achieved by embedding pitch-matched low-SNR analog process…
Understanding the Energy and Precision Requirements for Online Learning
Charbel Sakr, Ameya Patil, Sai Zhang +2
It is well-known that the precision of data, hyperparameters, and internal representations employed in learning systems directly impacts its energy, throughput, and latency. The pr…
Error-Resilient Machine Learning in Near Threshold Voltage via Classifier Ensemble
Sai Zhang, Naresh Shanbhag
In this paper, we present the design of error-resilient machine learning architectures by employing a distributed machine learning framework referred to as classifier ensemble (CE)…
Reducing the Energy Cost of Inference via In-sensor Information Processing
Sai Zhang, Mingu Kang, Charbel Sakr +1
There is much interest in incorporating inference capabilities into sensor-rich embedded platforms such as autonomous vehicles, wearables, and others. A central problem in the desi…