3 citations · 6 across the 15 of their papers we have counts for
8 papers · 1 filter
Clutch: High Performance Vector-Scalar Comparison using DRAM via Chunked Temporal Coding
Daichi Tokuda, Tatsuya Kubo, Ismail Emir Yuksel +8
Vector-scalar comparison is a fundamental computation primitive that compares each element in a vector against a single scalar value. It is widely used in various data-intensive wo…
PuDGhost: Experimental Analysis of Computation Result Corruption in Processing-using-DRAM Operations on Real DRAM Chips and Implications for Future Systems
Daichi Tokuda, İsmail Emir Yüksel, Tatsuya Kubo +7
Processing-using-DRAM (PuD) is a promising computation paradigm that alleviates frequent data movement between main memory and processing units by using each DRAM column as a compu…
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…
PACiM: A Sparsity-Centric Hybrid Compute-in-Memory Architecture via Probabilistic Approximation
Wenlun Zhang, Shimpei Ando, Yung-Chin Chen +3
Approximate computing emerges as a promising approach to enhance the efficiency of compute-in-memory (CiM) systems in deep neural network processing. However, traditional approxima…
HALO-CAT: A Hidden Network Processor with Activation-Localized CIM Architecture and Layer-Penetrative Tiling
Yung-Chin Chen, Shimpei Ando, Daichi Fujiki +2
To address the 'memory wall' problem in NN hardware acceleration, we introduce HALO-CAT, a software-hardware co-design optimized for Hidden Neural Network (HNN) processing. HALO-CA…