Lowering the barrier of entry to zero proof of knowledge

Ingonyama, a next-gen semiconductor collective designing accelerators for advanced cryptography, is coming out of stealth.

In an unusual move for a hardware company, they release all of their research to the public. Several articles have been published, including one that sparked the interest of the Ethereum community about Danksharding: A Mathematical Theory of Danksharding.

A Mathematical Theory of Danksharding

Danksharding is the proposed new sharding design for Ethereum, which introduces significant simplifications over previous designs.

One of the results of the Ingonyama report is the reinforcement of the notion that the dominant computing basic primitives required by Danksharding correlate strongly with those required for Zero-Knowledge-Proofs.

Ingonyama means Lion (in Zulu)

The adoption of zero-knowledge proofs

It is increasingly clear that Web3 will be the back-end of future financial environments and metaverses. These markets increasingly rely on Zero Knowledge Proofs (ZKPs) and increasingly want them to ensure both secure scaling and user privacy. The higher the scaling requirements of blockchain networks and the more complex the code running on them, the more ZKP computation presents a persistent constraint for users.

Cryptography has high computational demands that require increased use of specialized hardware acceleration. Creating a more cost-effective base layer of Zero Knowledge Proofs is key to their widespread adoption.

Ingonyama is working on exactly that: redesigning the hardware layer to improve the cost and energy efficiency of Zero Knowledge applications. Their mission is to forge the foundation upon which applications requiring Zero Knowledge Proof performance can be built, at greater speed and scale.

The ramifications of accessible ZKP will be a game-changer. Zero Knowledge processing will revolutionize a multitude of industries, from blockchains and the financial and insurance industries, to gaming, the metaverse and decentralized identity, to reimagining the way personal, medical and other data Sensitive data is shared over the web.

As the technology becomes accessible, many more applications leveraging ZKP will emerge as new builders create markets and serve their communities. This provides Ingonyama with a unique opportunity to make a difference in a range of use cases as we strive to make Zero Knowledge Proofs inexpensive, accessible, and fast.

Today, developers working on secure computing algorithms and Zero Knowledge Proofs have limited options for tools and programs. Zero Knowledge “Tier 1” hardware is the foundational layer of critical infrastructure needed to build a verifiable calculation software stack.

Verifiable computation allows programs to delegate processing to untrusted environments and then verify the accuracy of the returned results, all with greater efficiency than running the computation itself. This has major ramifications for decentralized applications, privacy preservation, and security.

A small number of talented teams and developers are starting the Zero Knowledge ecosystem today. Specially designed hardware will make the difference in lowering the barrier to entry.

Unconscious hardware acceleration

As general-purpose processors are pushed to their limits, the demand for faster computations and lower costs only increases. Hardware acceleration allows applications to offload certain computing tasks to specialized hardware, greatly improving their speed and capabilities.

GPUs, FPGAs, and ASICs are types of accelerators that enhance processing power depending on their purpose and design.

Much of Ingonyama’s current research leverages the benefits of FPGAs to develop proof-of-concepts related to ZK protocols. However, for many of the problems they aim to solve, the FPGA architecture becomes the bottleneck.

It is Ingonyama’s belief that Application Specific Integrated Circuits (ASICs) will eventually emerge as the most suitable hardware for implementing Zero Knowledge algorithms due to their superior performance related to power consumption. , throughput and latency.

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