3 citations · 6 across the 15 of their papers we have counts for
4 papers · 2 filters
PUDTune: Multi-Level Charging for High-Precision Calibration in Processing-Using-DRAM
Tatsuya Kubo, Daichi Tokuda, Lei Qu +2
Recently, practical analog in-memory computing has been realized using unmodified commercial DRAM modules. The underlying Processing-Using-DRAM (PUD) techniques enable high-through…
MVDRAM: Enabling GeMV Execution in Unmodified DRAM for Low-Bit LLM Acceleration
Tatsuya Kubo, Daichi Tokuda, Tomoya Nagatani +4
General matrix-vector multiplication (GeMV) remains a critical latency bottleneck in large language model (LLM) inference, even with quantized low-bit models. Processing-Using-DRAM…
Exploring the Versal AI Engine for 3D Gaussian Splatting
Kotaro Shimamura, Ayumi Ohno, Shinya Takamaeda-Yamazaki
Dataflow-oriented spatial architectures are the emerging paradigm for higher computation performance and efficiency. AMD Versal AI Engine is a commercial spatial architecture consi…
Accelerating Elliptic Curve Point Additions on Versal AI Engine for Multi-scalar Multiplication
Ayumi Ohno, Kotaro Shimamura, Shinya Takamaeda-Yamazaki
Multi-scalar multiplication (MSM) is crucial in cryptographic applications and computationally intensive in zero-knowledge proofs. MSM involves accumulating the products of scalars…