109 citations · 463 across the 26 of their papers we have counts for
9 papers · 1 filter
Search for Optimal Systolic Arrays: A Comprehensive Automated Exploration Framework and Lessons Learned
Jie Wang, Jason Cong
Systolic arrays have been widely used for accelerating HPC and deep learning applications. There is a plethora of previous works on the performance tuning of systolic arrays, but u…
Enabling Automated FPGA Accelerator Optimization Using Graph Neural Networks
Atefeh Sohrabizadeh, Yunsheng Bai, Yizhou Sun +1
High-level synthesis (HLS) has freed the computer architects from developing their designs in a very low-level language and needing to exactly specify how the data should be transf…
SPA-GCN: Efficient and Flexible GCN Accelerator with an Application for Graph Similarity Computation
Atefeh Sohrabizadeh, Yuze Chi, Jason Cong
While there have been many studies on hardware acceleration for deep learning on images, there has been a rather limited focus on accelerating deep learning applications involving…
Serpens: A High Bandwidth Memory Based Accelerator for General-Purpose Sparse Matrix-Vector Multiplication
Linghao Song, Yuze Chi, Licheng Guo +1
Sparse matrix-vector multiplication (SpMV) multiplies a sparse matrix with a dense vector. SpMV plays a crucial role in many applications, from graph analytics to deep learning. Th…
Pyxis: An Open-Source Performance Dataset of Sparse Accelerators
Linghao Song, Yuze Chi, Jason Cong
Specialized accelerators provide gains of performance and efficiency in specific domains of applications. Sparse data structures or/and representations exist in a wide range of app…
Optimal Qubit Mapping with Simultaneous Gate Absorption
Bochen Tan, Jason Cong
Before quantum error correction (QEC) is achieved, quantum computers focus on noisy intermediate-scale quantum (NISQ) applications. Compared to the well-known quantum algorithms re…
Sextans: A Streaming Accelerator for General-Purpose Sparse-Matrix Dense-Matrix Multiplication
Linghao Song, Yuze Chi, Atefeh Sohrabizadeh +3
Sparse-Matrix Dense-Matrix multiplication (SpMM) is the key operator for a wide range of applications, including scientific computing, graph processing, and deep learning. Architec…
Scaling Up Hardware Accelerator Verification using A-QED with Functional Decomposition
Saranyu Chattopadhyay, Florian Lonsing, Luca Piccolboni +12
Hardware accelerators (HAs) are essential building blocks for fast and energy-efficient computing systems. Accelerator Quick Error Detection (A-QED) is a recent formal technique wh…
TENET: A Framework for Modeling Tensor Dataflow Based on Relation-centric Notation
Liqiang Lu, Naiqing Guan, Yuyue Wang +5
Accelerating tensor applications on spatial architectures provides high performance and energy-efficiency, but requires accurate performance models for evaluating various dataflow…