The growing carbon footprint of artificial intelligence (AI) models, especially large ones such
as GPT-3, has been undergoing public scrutiny. Unfortunately, however, the equally …

The remarkable capabilities and intricate nature of Artificial Intelligence (AI) have
dramatically escalated the imperative for specialized AI accelerators. Nonetheless …

(Frankle & Carbin, 2019) shows that there exist winning tickets (small but critical
subnetworks) for dense, randomly initialized networks, that can be trained alone to achieve …

Neural network models, celebrated for their outstanding scalability and computational
capabilities, have demonstrated remarkable performance across various fields such as …

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

Adopting FPGA as an accelerator in datacenters is becoming mainstream for customized
computing, but the fact that FPGAs are hard to program creates a steep learning curve for …

High quality AI solutions require joint optimization of AI algorithms and their hardware
implementations. In this work, we are the first to propose a fully simultaneous, Efficient …

Tensor computations overwhelm traditional general-purpose computing devices due to the
large amounts of data and operations of the computations. They call for a holistic solution …

Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

The revolution of machine learning poses an unprecedented demand for computation
resources, urging more transistors on a single monolithic chip, which is not sustainable in …