安全可靠的 Raven Protocol 钱包
通过 Trezor 生态系统,全面自信地掌控您的Raven Protocol资产。
- 由您的硬件钱包保护
- 搭配兼容的热钱包使用
- 全球超 200 万用户信赖

收发您的 Raven Protocol 通过 Trezor Suite 应用
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支持Raven Protocol的 Trezor 硬件钱包
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支持的Raven Protocol网络
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- 您 100% 拥有您的加密货币
- 离线保护私钥,大幅降低在线攻击风险
- 您的数据 100% 匿名
- 您的加密货币不受任何公司控制
在线交易所
- 若交易所出现问题,您将失去加密货币
- 交易所是黑客攻击的目标
- 您的个人数据可能会被暴露
- 您并不真正拥有您的加密货币
如何在 Trezor 上使用RAVEN
请连接您的 Trezor 设备
打开第三方钱包应用
管理您的资产
充分利用您的 RAVEN
Trezor 助您安心管理 RAVEN
受安全芯片保护对抗在线与离线威胁的可靠防线
您的代币,您做主通过设备确认,全面掌控每笔交易
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从第一天起就信心十足包装与设备防拆封条共同保障 Trezor 设备完整性
Raven Protocol's specific use case is to perform AI training where speed is the key. We're taking a 1M image dataset that takes 2-3 weeks to train on AWS down to 2-3 hours on Raven. AI companies will be able to train models better and faster.
Raven Protocol is creating a self-sustaining and dynamic ecosystem for:
Customers who want to train their AI engines; and/or Contributors who would like to share their compute resources in the form of Computers, Smartphones, or even a server rack. Raven Tokens (RAVEN) will work as the common ground to facilitate a secure transaction that will take place inside our ecosystem. Enterprise clients who want to rent compute power will do so with RAVEN and contributors of the compute power will be rewarded in RAVEN.
Raven is creating a network of compute nodes that utilize idle compute power for the purposes of AI training where speed is the key. A native token is the key to bootstrapping a nascent network.
We want to incentivize and reward people all over the world to contribute their compute power to our network. Additionally, we will reward token holders for running masternodes which will be responsible for orchestrating the training of various deep neural networks.
Our consensus mechanism is something we call Proof-of-Calculation. Proof-of-Calculation will be the primary guideline for the regulation and distribution of incentives to the compute nodes in the network. Following are the two prime deciders for the incentive distribution:
Speed: Depending upon how fast a node can perform gradient calculations (in a neural network) and return it back to the Gradient Collector.
Redundancy: The 3 fastest redundant calculation will only qualify for receiving the incentive. This will make sure that the gradients that are getting returned are genuine and of the highest quality.