Jump Crypto Engineering 🔥💃🏻

Engineering and R&D at @jump_

[Jump Crypto Engineering 🔥💃🏻 @jumpengineering]

1/ Accelerating Number Theoretic Transforms is core to making ZK proof generation efficient.

We’re excited to share CycloneNTT: a novel architecture for Accelerating NTTs on FPGA

Check out the paper: https://t.co/91MWONRepI

And the blog post: https://t.co/8AEQqLMfin

🧵👇

[Jump Crypto 🔥💃🏻 @jump_]

1/ We're excited to open-source a library of optimized gadgets for Plonky2:

https://t.co/bDK0EwdfWZ

These include sha256, keccak256, and the underlying binary and biguint arithmetic. We hope these will help speed up your zero-knowledge proofs and tackle more complex ones!

[Emanuele Cesena @0x0ece]

Excited to share some of our work on ZKP!

Open source library of gadgets for Plonky2:
https://t.co/xxf9BLdQYL

Sha256, Keccak256, and the underlying binary & biguint arithmetic.

Heavily optimized: 5x sha2/s on my Mac M1 Pro 🚀

[Firedancer 🔥💃🏻 @jump_firedancer]

Introducing Firedancer’s latest technical milestone: fd_quic 🔥💃

A high-performance implementation of the QUIC and Solana transaction ingest network protocols.

1/15

[Figment Capital @FigmentCapital]

How to Accelerate Zero-Knowledge Proofs (ZKPs). Our new research piece by @tracecrypto1: https://t.co/Y6iFHYwXyK

[Jump Crypto 🔥💃🏻 @jump_]

Later this week we’ll sit down with a few of the teams attending zkWeek to discuss the state of ZK and more.

We’ll be joined by @ConsenSys, @MinaProtocol, and @lagrangedev.

Set a reminder below!

https://t.co/PEl390D9BS

[Firedancer 🔥💃🏻 @jump_firedancer]

Get the scoop on the Firedancer team’s first testnet milestone “Traction Ingest” in the demo+code overview video below. 🔥💃

https://t.co/yklNy9wa3g

[Firedancer 🔥💃🏻 @jump_firedancer]

1/ Earlier this week, the Firedancer team reached their first milestone: Transaction Ingest.

Here’s what it means 👇🔥💃

[Firedancer 🔥💃🏻 @jump_firedancer]

Join @fd_ripatel, @DanPaul000, and Jump Crypto Research and Development Architect, Philip Taffet to discuss the team’s progress building the Firedancer validator this Friday!

Set a reminder below. 🔥💃

https://t.co/AuwriIYRA4

[Jump Crypto Engineering 🔥💃🏻 @jumpengineering]

Jump Crypto Security Engineer @0x0ece recently presented on FPGA Acceleration of Multi-Scalar Multiplication on a live stream hosted by @Scroll_ZKP.

Watch the video below:
https://t.co/v1YTA0RlF9

[Jump Crypto Engineering 🔥💃🏻 @jumpengineering]

Hear what @rahulmaganti_ had to say on Manta Network’s OpenZL Talk 10:
CycloneNTT — Improving Twiddle Access for Number Theoretic Transforms. https://t.co/WhVX3wtiJC

[Manta Network (🔱,🔱) @MantaNetwork]

And finally, @rahulmaganti_ from @jump_ speaks on Improving “Twiddle Access for Number Theoretic Transforms,” by using a state-of-the-art butterfly network and maps the dataflow to hybrid FIFOs composed of on-chip SRAM and external memory. https://t.co/8iWXRHbQfR

[Firedancer 🔥💃🏻 @jump_firedancer]

Firedancer Friday…set a reminder. 👇

https://t.co/eoNgUP88Rt

[Firedancer 🔥💃🏻 @jump_firedancer]

1/ We strive for performance, but our highest priorities are reliability and security.

Learn more about the steps we are taking to help improve the Solana network: https://t.co/LdM0yKJMQT

[Jump Crypto Engineering 🔥💃🏻 @jumpengineering]

13/ The code will be open-sourced and available soon! If you want to chat ZK hardware acceleration, reach out to @rahulmaganti_, @0x0ece, and @nickraystalder.

https://t.co/GrK6F6ZqvB

[IACR @IACR_News]

#ePrint CycloneNTT: An NTT/FFT Architecture Using Quasi-Streaming of Large Datasets on DDR- and HBM-based FPGA Platforms: K Aasaraai, E Cesena, R Maganti, N Stalder, J Varela, K Bowers https://t.co/N0IjQ1YBGj

[Jump Crypto Engineering 🔥💃🏻 @jumpengineering]

1/ Accelerating Number Theoretic Transforms is core to making ZK proof generation efficient.

We’re excited to share CycloneNTT: a novel architecture for Accelerating NTTs on FPGA

Check out the paper: https://t.co/91MWONRepI

And the blog post: https://t.co/8AEQqLMfin

🧵👇

[Jump Crypto Engineering 🔥💃🏻 @jumpengineering]

12/ We explored architectures that process multiple layers at a time. Experiments with 1, 3, and 6-layers showed that the 3-layers approach yields the best latency vs. power tradeoff.