36 citations · 45 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…