83 citations · 94 across the 18 of their papers we have counts for
4 papers · 1 filter
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation
Nastaran Darabi, Maeesha Binte Hashem, Hongyi Pan +3
The edge processing of deep neural networks (DNNs) is becoming increasingly important due to its ability to extract valuable information directly at the data source to minimize lat…
Memory-Immersed Collaborative Digitization for Area-Efficient Compute-in-Memory Deep Learning
Shamma Nasrin, Maeesha Binte Hashem, Nastaran Darabi +4
This work discusses memory-immersed collaborative digitization among compute-in-memory (CiM) arrays to minimize the area overheads of a conventional analog-to-digital converter (AD…
MF-Net: Compute-In-Memory SRAM for Multibit Precision Inference using Memory-immersed Data Conversion and Multiplication-free Operators
Shamma Nasrin, Diaa Badawi, Ahmet Enis Cetin +2
We propose a co-design approach for compute-in-memory inference for deep neural networks (DNN). We use multiplication-free function approximators based on ell_1 norm along with a c…
Low Latency CMOS Hardware Acceleration for Fully Connected Layers in Deep Neural Networks
Nick Iliev, Amit Ranjan Trivedi
We present a novel low latency CMOS hardware accelerator for fully connected (FC) layers in deep neural networks (DNNs). The FC accelerator, FC-ACCL, is based on 128 8x8 or 16x16 p…