Design principles for lifelong learning AI accelerators
Lifelong learning—an agent's ability to learn throughout its lifetime—is a hallmark of
biological learning systems and a central challenge for artificial intelligence (AI). The …
biological learning systems and a central challenge for artificial intelligence (AI). The …
Ten lessons from three generations shaped google's tpuv4i: Industrial product
Google deployed several TPU generations since 2015, teaching us lessons that changed
our views: semi-conductor technology advances unequally; compiler compatibility trumps …
our views: semi-conductor technology advances unequally; compiler compatibility trumps …
Spatten: Efficient sparse attention architecture with cascade token and head pruning
The attention mechanism is becoming increasingly popular in Natural Language Processing
(NLP) applications, showing superior performance than convolutional and recurrent …
(NLP) applications, showing superior performance than convolutional and recurrent …
Bts: An accelerator for bootstrappable fully homomorphic encryption
Homomorphic encryption (HE) enables the secure offloading of computations to the cloud by
providing computation on encrypted data (ciphertexts). HE is based on noisy encryption …
providing computation on encrypted data (ciphertexts). HE is based on noisy encryption …
Silicon photonics for extreme scale systems
High-performance systems are increasingly bottlenecked by the growing energy and
communications costs of interconnecting numerous compute and memory resources …
communications costs of interconnecting numerous compute and memory resources …
DNN+ NeuroSim V2. 0: An end-to-end benchmarking framework for compute-in-memory accelerators for on-chip training
DNN+ NeuroSim is an integrated framework to benchmark compute-in-memory (CIM)
accelerators for deep neural networks, with hierarchical design options from device-level, to …
accelerators for deep neural networks, with hierarchical design options from device-level, to …
Ark: Fully homomorphic encryption accelerator with runtime data generation and inter-operation key reuse
Homomorphic Encryption (HE) is one of the most promising post-quantum cryptographic
schemes that enable privacy-preserving computation on servers. However, noise …
schemes that enable privacy-preserving computation on servers. However, noise …
EnGN: A high-throughput and energy-efficient accelerator for large graph neural networks
Graph neural networks (GNNs) emerge as a powerful approach to process non-euclidean
data structures and have been proved powerful in various application domains such as …
data structures and have been proved powerful in various application domains such as …
Transpim: A memory-based acceleration via software-hardware co-design for transformer
Transformer-based models are state-of-the-art for many machine learning (ML) tasks.
Executing Transformer usually requires a long execution time due to the large memory …
Executing Transformer usually requires a long execution time due to the large memory …
SHARP: A short-word hierarchical accelerator for robust and practical fully homomorphic encryption
Fully homomorphic encryption (FHE) is an emerging cryptographic technology that
guarantees the privacy of sensitive user data by enabling direct computations on encrypted …
guarantees the privacy of sensitive user data by enabling direct computations on encrypted …