Research for Practice: Cryptocurrencies, Blockchains, and Smart Contracts; Hardware for Deep Learning

Arvind Narayanan; Andrew Miller; Song Han

doi:10.1145/3028687.3043967

Research for Practice: Cryptocurrencies, Blockchains, and Smart Contracts; Hardware for Deep Learning

Arvind Narayanan
, Andrew Miller
, Song Han

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

First, Arvind Narayanan and Andrew Miller, co-authors of the increasingly popular open-access Princeton Bitcoin textbook, provide an overview of ongoing research in cryptocurrencies. Second, Song Han provides an overview of hardware trends related to another long-studied academic problem that has recently seen an explosion in popularity: deep learning.

Original language	English (US)
Journal	Queue
Volume	14
Issue number	6
DOIs	https://doi.org/10.1145/3028687.3043967
State	Published - Dec 1 2016

All Science Journal Classification (ASJC) codes

General Computer Science

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10.1145/3028687.3043967

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title = "Research for Practice: Cryptocurrencies, Blockchains, and Smart Contracts; Hardware for Deep Learning",

abstract = "First, Arvind Narayanan and Andrew Miller, co-authors of the increasingly popular open-access Princeton Bitcoin textbook, provide an overview of ongoing research in cryptocurrencies. Second, Song Han provides an overview of hardware trends related to another long-studied academic problem that has recently seen an explosion in popularity: deep learning.",

author = "Arvind Narayanan and Andrew Miller and Song Han",

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AU - Han, Song

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AB - First, Arvind Narayanan and Andrew Miller, co-authors of the increasingly popular open-access Princeton Bitcoin textbook, provide an overview of ongoing research in cryptocurrencies. Second, Song Han provides an overview of hardware trends related to another long-studied academic problem that has recently seen an explosion in popularity: deep learning.

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Research for Practice: Cryptocurrencies, Blockchains, and Smart Contracts; Hardware for Deep Learning

Abstract

All Science Journal Classification (ASJC) codes

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