9 citations · 13 across the 4 of their papers we have counts for
4 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…
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…