5 papers
SCALE-Sim EVA: Design Principles for an Extensible, Visualizable, and Adaptable Accelerator Simulation Framework
Jingtian Dang, Ritik Raj, Tushar Krishna
Modern AI accelerators increasingly combine heterogeneous compute units, hierarchical memories, local buffers, and specialized data movement paths. This diversity makes fixed accel…
Enabling AI ASICs for Zero Knowledge Proof
Jianming Tong, Jingtian Dang, Simon Langowski +6
Zero-knowledge proof (ZKP) provers remain costly because multi-scalar multiplication (MSM) and number-theoretic transforms (NTTs) dominate runtime as they need significant computat…
SCALE-Sim TPU: Validating and Extending SCALE-Sim for TPUs
Jingtian Dang, Ritik Raj, Changhai Man +2
Cycle-accurate simulators are widely used to study systolic accelerators, yet their accuracy and usability are often limited by weak validation against real hardware and poor integ…
Leveraging ASIC AI Chips for Homomorphic Encryption
Jianming Tong, Tianhao Huang, Jingtian Dang +9
Homomorphic Encryption (HE) provides strong data privacy for cloud services but at the cost of prohibitive computational overhead. While GPUs have emerged as a practical platform f…
Accurate Low-Degree Polynomial Approximation of Non-polynomial Operators for Fast Private Inference in Homomorphic Encryption
Jianming Tong, Jingtian Dang, Anupam Golder +3
As machine learning (ML) permeates fields like healthcare, facial recognition, and blockchain, the need to protect sensitive data intensifies. Fully Homomorphic Encryption (FHE) al…