113 citations · 290 across the 25 of their papers we have counts for
7 papers · 1 filter
Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance
Nandan Kumar Jha, Sparsh Mittal
In recent years, researchers have focused on reducing the model size and number of computations (measured as "multiply-accumulate" or MAC operations) of DNNs. The energy consumptio…
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs
Nandan Kumar Jha, Sparsh Mittal, Binod Kumar +1
The remarkable predictive performance of deep neural networks (DNNs) has led to their adoption in service domains of unprecedented scale and scope. However, the widespread adoption…
On the Demystification of Knowledge Distillation: A Residual Network Perspective
Nandan Kumar Jha, Rajat Saini, Sparsh Mittal
Knowledge distillation (KD) is generally considered as a technique for performing model compression and learned-label smoothing. However, in this paper, we study and investigate th…
DRACO: Co-Optimizing Hardware Utilization, and Performance of DNNs on Systolic Accelerator
Nandan Kumar Jha, Shreyas Ravishankar, Sparsh Mittal +3
The number of processing elements (PEs) in a fixed-sized systolic accelerator is well matched for large and compute-bound DNNs; whereas, memory-bound DNNs suffer from PE underutili…
ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks
Rajat Saini, Nandan Kumar Jha, Bedanta Das +2
The capability of the self-attention mechanism to model the long-range dependencies has catapulted its deployment in vision models. Unlike convolution operators, self-attention off…
E2GC: Energy-efficient Group Convolution in Deep Neural Networks
Nandan Kumar Jha, Rajat Saini, Subhrajit Nag +1
The number of groups () in group convolution (GConv) is selected to boost the predictive performance of deep neural networks (DNNs) in a compute and parameter efficient manner.…
The Ramifications of Making Deep Neural Networks Compact
Nandan Kumar Jha, Sparsh Mittal, Govardhan Mattela
The recent trend in deep neural networks (DNNs) research is to make the networks more compact. The motivation behind designing compact DNNs is to improve energy efficiency since by…