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Towards General-Purpose Decentralized Computing with Permissionless Extensibility

Publications associées (46)

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CALYPSO: Private Data Management for Decentralized Ledgers

Bryan Alexander Ford, Linus Gasser, Eleftherios Kokoris Kogias, Philipp Svetolik Jovanovic, Enis Ceyhun Alp

Distributed ledgers provide high availability and integrity, making them a key enabler for practical and secure computation of distributed workloads among mutually distrustful parties. Many practical applications also require strong confidentiality, howeve ...

ASSOC COMPUTING MACHINERY2020

Rethinking General-Purpose Decentralized Computing

Bryan Alexander Ford, Eleftherios Kokoris Kogias, Georgia Fragkouli, Enis Ceyhun Alp

While showing great promise, smart contracts are difficult to program correctly, as they need a deep understanding of cryptography and distributed algorithms, and offer limited functionality, as they have to be deterministic and cannot operate on secret da ...

ASSOC COMPUTING MACHINERY2019

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We present a method to train self-binarizing neural networks, that is, networks that evolve their weights and activations during training to become binary. To obtain similar binary networks, existing methods rely on the sign activation function. This funct ...

arXiv2019

Secure, Confidential Blockchains Providing High Throughput and Low Latency

Eleftherios Kokoris Kogias

One of the core promises of blockchain technology is that of enabling trustworthy data dissemination in a trustless environment. What current blockchain systems deliver, however, is slow dissemination of public data, rendering blockchain technology unusabl ...

EPFL2019

The slowdown of Moore's law, which has been the driving force of the electronics industry over the last 5 decades, is causing serious problem to Integrated Circuits (ICs) improvements. Technology scaling is becoming more and more complex and fabrication co ...

EPFL2018

Training DNNs with Hybrid Block Floating Point

Babak Falsafi, Martin Jaggi, Tao Lin, Mario Paulo Drumond Lages De Oliveira

The wide adoption of DNNs has given birth to unrelenting computing requirements, forcing datacenter operators to adopt domain-specific accelerators to train them. These accelerators typically employ densely packed full-precision floating-point arithmetic t ...

2018

Training DNNs with Hybrid Block Floating Point

Babak Falsafi, Martin Jaggi, Tao Lin, Mario Paulo Drumond Lages De Oliveira

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

OmniLedger: A Secure, Scale-Out, Decentralized Ledger via Sharding

Bryan Alexander Ford, Linus Gasser, Eleftherios Kokoris Kogias, Philipp Svetolik Jovanovic, Nicolas Gailly

Designing a secure permissionless distributed ledger (blockchain) that performs on par with centralized payment processors, such as Visa, is a challenging task. Most existing distributed ledgers are unable to scale-out, i.e., to grow their total processing ...

2018

Towards a Compiler for Reals

Viktor Kuncak, Eva Darulova

Numerical software, common in scientific computing or embedded systems, inevitably uses a finite-precision approximation of the real arithmetic in which most algorithms are designed. In many applications, the roundoff errors introduced by finite-precision ...

2017

Implicit Consensus: Blockchain with Unbounded Throughput

Kelong Cong

Recently, the blockchain technique was put in the spotlight as it introduced a systematic approach for multiple parties to reach consensus without needing trust. However, the application of this technique in practice is severely restricted due to its limit ...

2017