arXiv subjects

Rodrigo Otoni

Publications and source records attributed to Rodrigo Otoni.

3 recordsLinked to original sources

arXiv2026

Scalable datacenter replication with mostly-synchronous consensus on hardware

Consistent replication of data among distributed processes -- a task involving the well-known consensus problem -- is notoriously expensive and hard to scale, affecting especially datacenter services with stringent performance requirements. To mitigate this problem, we introduce scalable replication in-hardware ( scarHW ): a network card design that improves throughput and latency of consistent replication even when increasing the number of replicas, whereas current systems operate at a small scale or with relaxed consistency guarantees. At the heart of scarHW is our novel POPUC consensus algorithm, implemented in an FPGA smartNIC to take full advantage of the "mostly synchronous" behavior of programmable network devices in the datacenter. Unlike widely-adopted "mostly asynchronous" coordination protocols such as Paxos or leaderless alternatives, POPUC implements a generalized variant of consensus dubbed collaborative consensus which allows for several simultaneous decisions, achieving great scalability without compromising availability. POPUC preserves safety guarantees in the presence of process crash-stop and message send/receive omission failures (capturing incidental asynchrony) and has been formally specified and verified in TLA+. Our FPGA prototype improves throughput and latency of widely-used services Redis and Zookeeper by up to two orders of magnitude compared to the state of the art. scarHW-based services also achieve zero downtime upon failure of a minority of replicas, offering a highly-robust, wire-speed, scalable replication system.

cs.DC↗

arXiv2025

Monomorphism-based CGRA Mapping via Space and Time Decoupling

Coarse-Grain Reconfigurable Arrays (CGRAs) provide flexibility and energy efficiency in accelerating compute-intensive loops. Existing compilation techniques often struggle with scalability, unable to map code onto large CGRAs. To address this, we propose a novel approach to the mapping problem where the time and space dimensions are decoupled and explored separately. We leverage an SMT formulation to traverse the time dimension first, and then perform a monomorphism-based search to find a valid spatial solution. Experimental results show that our approach achieves the same mapping quality of state-of-the-art techniques while significantly reducing compilation time, with this reduction being particularly tangible when compiling for large CGRAs. We achieve approximately $10^5\times$ average compilation speedup for the benchmarks evaluated on a $20\times 20$ CGRA.

cs.AR↗

arXiv2025

An Improved Template for Approximate Computing

Deploying neural networks on edge devices entails a careful balance between the energy required for inference and the accuracy of the resulting classification. One technique for navigating this tradeoff is approximate computing: the process of reducing energy consumption by slightly reducing the accuracy of arithmetic operators. In this context, we propose a methodology to reduce the area of the small arithmetic operators used in neural networks - i.e., adders and multipliers - via a small loss in accuracy, and show that we improve area savings for the same accuracy loss w.r.t. the state of the art. To achieve our goal, we improve on a boolean rewriting technique recently proposed, called XPAT, where the use of a parametrisable template to rewrite circuits has proved to be highly beneficial. In particular, XPAT was able to produce smaller circuits than comparable approaches while utilising a naive sum of products template structure. In this work, we show that template parameters can act as proxies for chosen metrics and we propose a novel template based on parametrisable product sharing that acts as a close proxy to synthesised area. We demonstrate experimentally that our methodology converges better to low-area solutions and that it can find better approximations than both the original XPAT and two other state-of-the-art approaches.

cs.LG↗