36 citations · 44 across the 10 of their papers we have counts for
11 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…
Flint: Compiler Enabled Cluster-Free Design Space Exploration for Distributed ML
Jinsun Yoo, Meghan Cowan, Zheng Du +3
Design space exploration for future distributed Machine Learning systems suffers from a lack of readily available workload representation that enables flexible exploration across t…
Privatar: Scalable Privacy-preserving Multi-user VR via Secure Offloading
Jianming Tong, Hanshen Xiao, Krishna Kumar Nair +5
Multi-user virtual reality enables immersive interaction. However, rendering avatars for numerous participants on each headset incurs prohibitive computational overhead, limiting s…
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…
MINISA: Minimal Instruction Set Architecture for Next-gen Reconfigurable Inference Accelerator
Jianming Tong, Devansh Jain, Yujie Li +2
Modern reconfigurable AI accelerators rely on rich mapping and data-layout flexibility to sustain high utilization across matrix multiplication, convolution, and emerging applicati…
AIRCHITECT v2: Learning the Hardware Accelerator Design Space through Unified Representations
Jamin Seo, Akshat Ramachandran, Yu-Chuan Chuang +2
Design space exploration (DSE) plays a crucial role in enabling custom hardware architectures, particularly for emerging applications like AI, where optimized and specialized desig…
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…
Real-time Digital RF Emulation -- II: A Near Memory Custom Accelerator
Mandovi Mukherjee, Xiangyu Mao, Nael Rahman +16
A near memory hardware accelerator, based on a novel direct path computational model, for real-time emulation of radio frequency systems is demonstrated. Our evaluation of hardware…
FEATHER: A Reconfigurable Accelerator with Data Reordering Support for Low-Cost On-Chip Dataflow Switching
Jianming Tong, Anirudh Itagi, Prasanth Chatarasi +1
The inference of ML models composed of diverse structures, types, and sizes boils down to the execution of different dataflows (i.e. different tiling, ordering, parallelism, and sh…
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…