Sluice: Network-Wide Data Plane Programming. - Vikas Natesh; Pravein Govindan Kannan; Anirudh Sivaraman; Ravi Netravali (SIGCOMM Posters and Demos, 2019)

CAKE: matrix multiplication using constant-bandwidth blocks. - H. T. Kung 001; Vikas Natesh; Andrew Sabot (SC, 2021)

MEMA Runtime Framework: Minimizing External Memory Accesses for TinyML on Microcontrollers. - Andrew Sabot; Vikas Natesh; H. T. Kung 001; Wei-Te Ting (CoRR, 2023)

Rosko: Row Skipping Outer Products for Sparse Matrix Multiplication Kernels. - Vikas Natesh; Andrew Sabot; H. T. Kung 001; Mark Ting (CoRR, 2023)

Alternating Greedy Schedules: Enabling Low-Bitwidth Accumulation of Dot Products in Neural Network Computations. - Vikas Natesh; H. T. Kung 001 (ISCAS, 2025)

PQS (Prune, Quantize, and Sort): Low-Bitwidth Accumulation of Dot Products in Neural Network Computations. - Vikas Natesh; H. T. Kung 001 (CoRR, 2025)

MGS: Markov Greedy Sums for Accurate Low-Bitwidth Floating-Point Accumulation. - Vikas Natesh; H. T. Kung 001; David Kong (CoRR, 2025)