9 citations · 14 across the 5 of their papers we have counts for
5 papers
Fundamental Limits on Energy-Delay-Accuracy of In-memory Architectures in Inference Applications
Sujan Kumar Gonugondla, Charbel Sakr, Hassan Dbouk +1
This paper obtains fundamental limits on the computational precision of in-memory computing architectures (IMCs). An IMC noise model and associated SNR metrics are defined and thei…
Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks
Charbel Sakr, Naigang Wang, Chia-Yu Chen +4
Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-pr…
Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm
Charbel Sakr, Naresh Shanbhag
The high computational and parameter complexity of neural networks makes their training very slow and difficult to deploy on energy and storage-constrained computing systems. Many…
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…
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…