Название: Cross-Layer Approximation and In-Network Acceleration: Enabling the Next Generation of Sustainable and High-Performance Reconfigurable Systems
Автор: Zahra Ebrahimi, Akash Kumar
Издательство: Springer
Год: 2026
Страниц: 230
Язык: английский
Формат: pdf (true), epub
Размер: 68.5 MB

This book presents a novel Cross-Layer Approximation and Distribution architecture and methodology that advances the design of high-performance, high-throughput, energy-efficient, and sustainable reconfigurable computing systems. By leveraging the error tolerance inherent in modern AI and signal processing workloads, it enables performance and energy gains across hardware and software layers. The authors introduce innovative approximate multipliers, dividers, and coarse-grained processing elements for FPGA and CGRA platforms, coupled with an error-resiliency analysis and a heuristic-driven optimization framework that dynamically balance performance and accuracy. Extending beyond conventional architectures, the methodology described also integrates novel In-Network Computing (INC) techniques to bring computation closer to data sources within 5G/6G infrastructures. The result is a cohesive, scalable approach that redefines how energy-efficient and adaptive computing can be achieved across the edge-to-cloud continuum.

Historically, most approximation techniques have been developed for Application-Specific Integrated Circuits (ASICs). Yet, ASICs suffer from long development cycles and substantial Non-Recurring Engineering (NRE) costs. Reconfigurable platforms such as Field-Programmable Gate Arrays (FPGAs) and Coarse-Grained Reconfigurable Architectures (CGRAs) mitigate these drawbacks. Nevertheless, direct adoption of ASIC-oriented approximation methods on reconfigurable hardware fails to deliver proportional performance gains due to fundamental architectural differences. Therefore, approximation strategies must be explicitly designed or adapted for reconfigurable fabrics. Moreover, most circuit-level approximation work has concentrated on addition and multiplication. Although less frequent, division remains unavoidable in many error-resilient workloads. Division operations incur higher area and energy costs and exhibit significantly greater latency than multiplication, often limiting overall application performance. These challenges also make divider integration within CGRA Processing Elements (PEs) problematic. Incorporating a resource-intensive divider into a PE substantially increases area, power consumption, and reduces operating frequency. Consequently, many approximate CGRA architectures omit divider support entirely, preventing division-containing kernels from being offloaded to the CGRA and forcing the host processor to remain involved for these operations. Furthermore, nearly all existing approximate multipliers and dividers are SISD and nonpipelined, leaving the potential for higher throughput untapped. In addition, many state-of-the-art cross-layer approximation approaches neglect architectural-level optimization opportunities and fail to construct a cohesive stack of techniques across abstraction layers. Existing cross-layer approaches for multi-kernel applications have primarily targeted Neural Networks (NNs), leaving numerous other domains unexplored. A general methodology capable of jointly tuning multiple approximation knobs across layers for multi-kernel workloads is still missing. A key prerequisite—kernel-level error-resilience sensitivity analysis quantifying end-to-end performance versus Quality of Results (QoR) trade-offs—has also been largely overlooked.

Describes a unified perspective on how approximation techniques can be applied across multiple abstraction levels;
Discusses how FPGAs, CGRAs and In-Network Computing can enable scalable, distributed, and low-latency computation;
Introduces architectural designs such as approximate multipliers, dividers, and hybrid SIMD/MIMD processing elements.

Contents: