Fully Homomorphic Encryption (FHE) enables offloading computation to untrusted servers
with cryptographic privacy. Despite its attractive security, FHE is not yet widely adopted due …

Recent advances in state-of-the-art DNN architecture design have been moving toward
Transformer models. These models achieve superior accuracy across a wide range of …

Hardware specialization is a promising trend to sustain performance growth. Spatial
hardware accelerators that employ specialized and hierarchical computation and memory …

In recent years, many accelerators have been proposed to efficiently process sparse tensor
algebra applications (eg, sparse neural networks). However, these proposals are single …

With the increasing size of DNN models and the growing discrepancy between compute
performance and memory bandwidth, fusing multiple layers together to reduce off-chip …

With the continuous expansion of the DNN accelerator scale, inter-layer scheduling, which
studies the allocation of computing resources to each layer and the computing order of all …

Machine learning models with various tensor operators are becoming ubiquitous in recent
years. There are two types of operators in machine learning: compute-intensive operators …

In the hardware design space exploration process, it is critical to optimize both hardware
parameters and algorithm-to-hardware mappings. Previous work has largely approached …

As Deep Learning continues to drive a variety of applications in edge and cloud data
centers, there is a growing trend towards building large accelerators with several sub …

Over the past few years, the explosion in sparse tensor algebra workloads has led to a
corresponding rise in domain-specific accelerators to service them. Due to the irregularity …