Privacy tech isn’t just for cryptographers in smoke-filled rooms; it’s inching toward the everyday, like a quiet upgrade that makes your devices subtly kinder to your data. At the heart of this movement are zero-knowledge proofs (ZKPs): clever math tricks that let you prove you know something without revealing the secret itself. That capability has already begun seeping into authentication, data ownership, and privacy-preserving AI workflows. Yet turning those proofs into something fast, affordable, and widely usable has proven stubborn. The bottleneck isn’t the idea—it’s the hardware and the way we crunch numbers inside the usual CPUs. HashEmAll is a bold move to change that equation by bringing three ZK-friendly hash functions to reconfigurable hardware, specifically field-programmable gate arrays (FPGAs), with performance that competes with or even surpasses traditional hashing on consumer devices.
The work behind HashEmAll comes from the University of California, San Diego, and Arizona State University, with lead authors Nojan Sheybani and Tengkai Gong, and a cast that includes Anees Ahmed, Nges Brian Njungle, Michel Kinsy, and Farinaz Koushanfar. They’ve assembled an open-source library that implements three prominent ZK-friendly hashes—Rescue-Prime, Griffin, and Reinforced Concrete—on FPGA hardware. The punchline isn’t just a faster hash; it’s a blueprint for how to align the arithmetic inside ZK proofs with the hardware you actually have on your desk or in your pocket. In their experiments, HashEmAll delivers up to 23× speedups over optimized CPU implementations, while staying power-efficient and compatible with accessible FPGA boards. That combination matters because it makes ZK-friendly hashing practical for edge devices, not just data centers.
To understand why this matters, it helps to picture what a hash function does in a ZK proof system. Think of a hash as a compact, cryptographically disciplined blender: you mix inputs, and the result acts like a fingerprint that’s hard to reverse but easy to verify. In ZK systems, these hashes appear inside protocols that turn complex computations into polynomial recipes the verifier can check efficiently. Traditional hash functions—the kind we rely on day to day in secure messaging and web privacy—aren’t optimized for the kind of arithmetic ZK proofs prefer. They produce lots of constraints, which translates into heavier proving workloads and slower proofs. ZK-friendly hashes, by design, minimize that constraint count. HashEmAll doesn’t abandon this optimization; it shifts the weight to hardware so that even the plaintext computations stay fast on ordinary devices. That could be the difference between ZK proofs feeling optional and proofs feeling like a natural capability of your favorite apps.
Hashing for privacy: a new class of cryptographic helpers
HashEmAll isn’t a single trick; it’s a harmonized trio of ideas built around three distinct ZK-friendly hashes. Rescue-Prime belongs to the “low-degree equivalence” camp, a design philosophy that keeps the algebra relatively tame while preserving security. Griffin is its faster sibling, extending the same underlying idea with a Horst-inspired construction and a version of the nonlinear layer that in practice can be computed more quickly on hardware. Reinforced Concrete takes a different route entirely: a lookup-based design that shines when the proof system itself can support table lookups. Each hash operates over a BN254 prime field, a choice that aligns with many practical ZK setups and keeps the cryptographic groundwork solid.
To make that work in hardware, HashEmAll engineers a few standout moves. In Rescue-Prime and Griffin, the heart of the computation is a power-map operation—raising a field element to a fixed exponent—paired with a linear mix and a handful of round constants. On a CPU, exponentiation over a 254-bit field is expensive; on a carefully stitched FPGA, it becomes a sequence of modular multiplications that can be piped and parallelized. HashEmAll formalizes this with a square-and-multiply exponentiation engine that can be run with two multipliers in parallel when latency is the goal, or with more multipliers to boost throughput. The result is a highly optimized path from input to permutation within the hash, an essential building block for any sponge-based hashing scheme.
Reinforced Concrete, by contrast, leans on a different magic: a lookup-based design that decomposes field elements into smaller chunks, runs heavy parts of the computation through precomputed tables, and then recomposes the results. This is especially potent for ZK proofs that support lookups (a growing area in modern ZK systems), but it still needs flexible hardware to handle the nonlinear, degree-5 bricks and the Bars operation that stitches everything back together again. HashEmAll didn’t treat lookups as a loophole; they’re baked in as a first-class path, with hardware modules designed to exploit them when the surrounding proof system allows it. It’s a reminder that hardware design in this space isn’t about forcing a single architecture; it’s about giving developers a menu of approaches that fit different ZK ecosystems.
From CPU speed to FPGA acceleration
The second pillar of HashEmAll is the hardware-software co-design that makes all three hashes practical on real silicon. HashEmAll isn’t merely porting software to FPGA; it’s building a modular library of field-arithmetic blocks that can be reconfigured on the fly to support the three different hashes. At the core is a reconfigurable modular multiplier with modes tailored to the needs of the three algorithms: a MULT mode for quadratic and exponential computations, a DECOMPOSE mode that accelerates the Decomp step in RC, and a COMPOSE mode that reconstructs field elements after subfield operations. This isn’t just clever engineering jargon—it’s a way to reuse hardware blocks across different hash architectures, reducing both development time and silicon area while maintaining high throughput.
Another clever trick is how HashEmAll handles divisions, traditionally the nemesis of hardware efficiency. In RCπ’s Decomp step, division would be a bottleneck; so the team replaces division with a lookup-based reciprocal multiplied by a power-of-two shift. In practical terms, they precompute 1/si, scale it by a power of two for fast shifts, and store it in a LUT. The net effect is a division that behaves like a simple memory lookup, dramatically speeding up what would otherwise be a slow arithmetic detour. It’s a reminder that in hardware, the fastest math is often the math you don’t compute at run time, but the math you precompute and fetch at the speed of memory.
On the exponentiation side, the square-and-multiply engine is the star. For each round, it climbs the exponent bit by bit, squaring the base and conditionally multiplying by the input. The design choices reflect a classic hardware trade-off: you can optimize for area, using fewer multipliers and more clever scheduling; or you can optimize for latency, duplicating units to reduce the time it takes to finish a single permutation. HashEmAll presents both flavors for Rescue-Prime and Griffin, labeled A for area and L for latency, and does the same for Reinforced Concrete with dedicated configurations. The upshot is that you can tailor HashEmAll to a device the size of a laptop or a board the size of a small book and still get compelling ZK performance.
What this means for the near future
The numbers in the HashEmAll study aren’t just impressive on a lab bench; they’re a practical invitation. In their experiments, the authors measured performance on a Virtex UltraScale+ FPGA and pitted the results against CPU baselines derived from prior high-quality implementations. The latency-optimized designs achieved up to 23× speedups, while even the area-conscious designs in some configurations beat the CPU in throughput for batch workloads. In other words, this isn’t a novelty project; it’s a pathway to bringing ZK-friendly hashing to devices that touch everyday life—phones, routers, edge servers, and embedded devices—without demanding specialized hardware in every pocket of the internet.
There’s also a social and strategic layer. HashEmAll is open-source, designed to be integrated into real-world applications, and built with a mindset of interoperability with existing ZK proof systems that support lookups or standard sponge constructions. The authors explicitly position Rescue-Prime as the well-trodden, security-analysed option, while Griffin offers faster plaintext performance for those willing to take on newer, though still carefully studied, territory. Reinforced Concrete, with its lookups-friendly architecture, targets proof systems that embrace table-driven strategies. In short, HashEmAll offers a menu: you can pick a hash that lines up with your proof system’s capabilities and still reap strong plaintext and ZK performance on consumer hardware.
The paper also emphasizes a broader ambition: to lower the barrier to entry for privacy-preserving computation. The authors note that ZK proofs have become a backbone for authentication, data ownership, and privacy-preserving machine learning experiments. Hardware acceleration, they argue, may be the missing bridge that makes these techniques practical in real-time, on devices people actually own. When you can hash data and incorporate it into a proof quickly on a phone or a gateway, you begin to unlock previously impractical workflows—like streaming encrypted inferences that can be verified on-device or in a lightweight cloud edge without exposing private inputs. It’s not just about speed; it’s about enabling a world where privacy-preserving proofs don’t require a power-hungry data-center off-ramp to function.
There’s also a note of humility in the work. HashEmAll is described as a first realization of three ambitious hash families on reconfigurable hardware, and the authors call out the security evaluation differences among the candidates. Rescue-Prime, with a long track record and a robust bounty program, offers a conservative path for security-conscious teams. Griffin, meanwhile, embodies a forward-leaning push toward higher plaintext performance, inviting the cryptographic community to keep scrutinizing it as it matures. Reinforced Concrete represents a reminder that there are multiple routes to fast ZK hashing, including the lookups-friendly path that dovetails with certain proof systems. This isn’t a victory lap; it’s a hands-on invitation to experiment, adapt, and extend.
As a result, HashEmAll could become a cornerstone for a future where ZK proofs aren’t the exception but the default for privacy-preserving computation. The combination of three distinct hashing philosophies, tuned for hardware, is a pragmatic acknowledgment that the algebra of zero-knowledge has to live where computation actually happens. That means devices you already own—laptops, phones, routers—could soon be able to participate in private, verifiable computations at scale, without blinking at the power budget or the cost of specialized servers. The open-source nature of HashEmAll only accelerates this, letting systems architects mix and match with real-world proof systems and data workflows.
In sum, HashEmAll doesn’t just speed up hashes; it lubricates the machinery of privacy-preserving computation for the next generation of applications. If the dream of zero-knowledge proofs is to move from a cryptography conference to the kitchen-table realities of digital life, then HashEmAll is a compelling example of how hardware-software co-design can bridge that gap. The question it leaves hanging is not whether this is possible, but how quickly we’ll see these ideas folded into the apps, devices, and services that shape our online privacy in the years ahead.
Institutional note: The project is led by researchers at the University of California, San Diego and Arizona State University, with Nojan Sheybani and Tengkai Gong as co-leads and a team including Anees Ahmed, Nges Brian Njungle, Michel Kinsy, and Farinaz Koushanfar. HashEmAll is described as open-source and designed for easy integration into real-world ZKP applications, signaling a deliberate push to move advanced cryptography from theory into daily use.
