📃 Tech Deep Dive: CryptoCPU – Powering FHE & ZKPs for Confidential Computing Verifiable and confidential computation is no longer a futuristic concept — it's here, and it's advancing rapidly. These technologies are beginning to solve some of the key barriers preventing large enterprises and FinTech companies from embracing public blockchain infrastructure at scale. They might also enable many use cases in traditional industries such as privacy-preserving cloud computing and machine learning. At the core of this transformation are two powerful technologies: Fully Homomorphic Encryption (FHE) and Zero-Knowledge Proofs (ZKPs). 🔗 Read the full article here: ponos.technology/blog/tech-d… @PonosTechnology #FHE #ZeroKnowledge #ZK #ConfidentialComputing #Blockchain #Crypto #Privacy #HomomorphicEncryption #Web3 #Hardware

Milos Stanisavljevic of @PonosTechnology introduced the Ponos crypto CPU: 🔹 A specialized RISC-V processor for ZK workloads 🔹 4x vector cores 8x scalar cores modular arithmetic co-processors 🔹 Designed for proving execution: trace gen, NTT, ECDSA, modular exponentiation 🔹 Target: 16-core cards priced ~$1,200 proving Ethereum blocks in real-time 🔹 100× better energy efficiency vs. current GPU/CPU setups 🔹 ASIC planned for 2026

Real-Time Ethereum Proving is here. INTRODUCING: SP1 Hypercube

A common misconception about ASIC-based platforms is that they are fixed, single-function computing devices that cannot be reprogrammed or adapted for various tasks without a complete redesign. While this is true for ASICs designed with very narrow, specific instructions—such as SHA-256 hashing for Bitcoin mining—these devices typically have minimal firmware and simple communication interfaces, focusing exclusively on a single dedicated task. Now, imagine extending an ASIC’s capabilities to support more complex workloads, like Google Cloud Tensor Processing Units (TPUs). These devices remain ASICs at their core but are managed by low-level firmware and enhanced with a custom-built, domain-specific compiler stack. This allows them to efficiently handle a wide range of AI model computations rather than being limited to a single function. Taking this concept further, consider integrating a full CPU into an ASIC chip along with specialized extensions for specific operations. In this case, rather than completely redesigning the firmware, we would only need to optimize mainstream compilers to support these extensions. The result? A powerful, programmable device that can be used as a server card or a standalone unit, capable of running high-level programming languages while benefiting from native, dedicated instruction sets. This approach enables significantly faster execution than traditional computing architectures—similar to how ARM-based Snapdragon processors optimize performance in mobile devices. In our case, we extend the well-established RISC-V processor, leveraging its robust tooling and strong community support. This allows compatibility with mainstream programming languages while incorporating dedicated hardware support for major cryptographic operations, such as signature verification, hashing, and NTT/FFT computations. The outcome is an unprecedented reduction in computation time and cost for key blockchain operations.

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