Maximizing crosstalk-induced slowdown during path delay test. - Dibakar Gope; D. M. H. Walker (ICCD, 2012)

Atomic SC for simple in-order processors. - Dibakar Gope; Mikko H. Lipasti (HPCA, 2014)

Bias-Free Branch Predictor. - Dibakar Gope; Mikko H. Lipasti (MICRO, 2014)

Hash Map Inlining. - Dibakar Gope; Mikko H. Lipasti (PACT, 2016)

Architectural Support for Server-Side PHP Processing. - Dibakar Gope; David J. Schlais; Mikko H. Lipasti (ISCA, 2017)

The CURE: Cluster Communication Using Registers. - Vignyan Reddy Kothinti Naresh; Dibakar Gope; Mikko H. Lipasti (ACM Trans. Embed. Comput. Syst., 2017)

A Case for Scoped Persist Barriers in GPUs. - Dibakar Gope; Arkaprava Basu; Sooraj Puthoor; Mitesh Meswani (GPGPU@PPoPP, 2018)

Run-Time Efficient RNN Compression for Inference on Edge Devices. - Urmish Thakker; Jesse G. Beu; Dibakar Gope; Ganesh Dasika; Matthew Mattina (EMC2@HPCA/CVPR/ISCA, 2019)

Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications. - Dibakar Gope; Ganesh Dasika; Matthew Mattina (SysML, 2019)

Pushing the limits of RNN Compression. - Urmish Thakker; Igor Fedorov; Jesse G. Beu; Dibakar Gope; Chu Zhou; Ganesh Dasika; Matthew Mattina (EMC2@NeurIPS, 2019)

Compressing RNNs for IoT devices by 15-38x using Kronecker Products. - Urmish Thakker; Jesse G. Beu; Dibakar Gope; Chu Zhou; Igor Fedorov; Ganesh Dasika; Matthew Mattina (CoRR, 2019)

Ternary MobileNets via Per-Layer Hybrid Filter Banks. - Dibakar Gope; Jesse G. Beu; Urmish Thakker; Matthew Mattina (CVPR Workshops, 2020)

Rank and run-time aware compression of NLP Applications. - Urmish Thakker; Jesse G. Beu; Dibakar Gope; Ganesh Dasika; Matthew Mattina (SustaiNLP@EMNLP, 2020)

Pushing the Envelope of Dynamic Spatial Gating technologies. - Xueqin Huang; Urmish Thakker; Dibakar Gope; Jesse G. Beu (AIChallengeIoT@SenSys, 2020)

Understanding the Impact of Dynamic Channel Pruning on Conditionally Parameterized Convolutions. - Ravi Raju; Dibakar Gope; Urmish Thakker; Jesse G. Beu (AIChallengeIoT@SenSys, 2020)

The gem5 Simulator: Version 20.0+. - Jason Lowe-Power; Abdul Mutaal Ahmad; Ayaz Akram; Mohammad Alian; Rico Amslinger; Matteo Andreozzi; Adrià Armejach; Nils Asmussen; Srikant Bharadwaj; Gabe Black; Gedare Bloom; Bobby R. Bruce; et al. (CoRR, 2020)

High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands. - Dibakar Gope; Jesse G. Beu; Matthew Mattina (CoRR, 2020)

MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers. - Colby R. Banbury; Chuteng Zhou; Igor Fedorov; Ramon Matas Navarro; Urmish Thakker; Dibakar Gope; Vijay Janapa Reddi; Matthew Mattina; Paul N. Whatmough (MLSys, 2021)

Compressing RNNs to Kilobyte Budget for IoT Devices Using Kronecker Products. - Urmish Thakker; Igor Fedorov; Chu Zhou; Dibakar Gope; Matthew Mattina; Ganesh Dasika; Jesse G. Beu (ACM J. Emerg. Technol. Comput. Syst., 2021)

Super-Efficient Super Resolution for Fast Adversarial Defense at the Edge. - Kartikeya Bhardwaj; Dibakar Gope; James Ward; Paul N. Whatmough; Danny Loh (DATE, 2022)

Collapsible Linear Blocks for Super-Efficient Super Resolution. - Kartikeya Bhardwaj; Milos Milosavljevic; Liam O'Neil; Dibakar Gope; Ramon Matas Navarro; Alex Chalfin; Naveen Suda; Lingchuan Meng; Danny Loh (MLSys, 2022)

Restructurable Activation Networks. - Kartikeya Bhardwaj; James Ward; Caleb Tung; Dibakar Gope; Lingchuan Meng; Igor Fedorov; Alex Chalfin; Paul N. Whatmough; Danny Loh (CoRR, 2022)

CPT-V: A Contrastive Approach to Post-Training Quantization of Vision Transformers. - Natalia Frumkin; Dibakar Gope; Diana Marculescu (CoRR, 2022)

Jumping through Local Minima: Quantization in the Loss Landscape of Vision Transformers. - Natalia Frumkin; Dibakar Gope; Diana Marculescu (ICCV, 2023)

PerfSAGE: Generalized Inference Performance Predictor for Arbitrary Deep Learning Models on Edge Devices. - Yuji Chai; Devashree Tripathy; Chuteng Zhou; Dibakar Gope; Igor Fedorov; Ramon Matas Navarro; David Brooks 001; Gu-Yeon Wei; Paul N. Whatmough (CoRR, 2023)

Data-Free Group-Wise Fully Quantized Winograd Convolution via Learnable Scales. - Shuokai Pan; Gerti Tuzi; Sudarshan Sreeram; Dibakar Gope (CVPR, 2025)

Highly Optimized Kernels and Fine-Grained Codebooks for LLM Inference on Arm CPUs. - Dibakar Gope; David Mansell; Danny Loh; Ian Bratt (CoRR, 2025)