Beyond Peak TOPS/W: A System-Level Perspective on Hybrid Digital, Analogue and Neuromorphic Computing

2026-08-04

AI Summary

This work addresses the high energy consumption and data-movement bottlenecks confronting AI deployment in edge and distributed settings by proposing a digitally orchestrated hybrid computing architecture that synergistically integrates analog and neuromorphic computing. Physical computing units are selectively introduced only where they offer substantial energy-efficiency gains, while a unified digital control layer manages uncertainty and ensures fault tolerance. Departing from conventional peak TOPS/W metrics, the study establishes a deployment-oriented, system-level efficiency evaluation framework. By combining photonic computing, in-memory computing, and neuromorphic hardware with programmable digital control, mature software stacks, and comprehensive system-level energy accounting, the work delineates the architecture’s suitability for matrix operations and event-driven tasks, systematically uncovering its energy-efficiency potential, software requirements, and engineering challenges to provide both a theoretical foundation and practical pathway for efficient AI system design.

Abstract

The digital revolution, which progressively replaced analogue methods with digital circuits, has entered a new phase as AI expands across cloud infrastructure, mobile networks, wearables and physical systems, including drones and robots. Digital computing remains the general-purpose foundation of this expansion: it supports heterogeneous, on-device and decentralised AI through programmable control, mature software and decades of accumulated engineering infrastructure. Yet as energy and data-movement constraints become more significant, that same foundation is increasingly being extended rather than replaced by selected analogue and physical principles that it can host, configure and verify. Photonic, in-memory and neuromorphic architectures offer routes to reducing data movement and accelerating matrix-intensive and event-driven processing, not as alternatives to digital infrastructure but as specialised engines operating within it. This paper argues that hybrid digital--analogue computing represents a credible pathway towards more energy-efficient AI systems: one in which physical substrates earn an expanding role only where they deliver a measurable system-level advantage, under digital orchestration that manages integration, uncertainty and fallback. It examines the architectural principles, workload suitability, energy accounting, software requirements, limitations and open challenges associated with this transition, and argues that future progress should be evaluated through deployed-system metrics rather than isolated peak tera operations per second per watt (TOPS/W) claims.

Problem

• hybrid computing
• energy efficiency
• data movement
• neuromorphic computing
• system-level optimization

Innovation

• hybrid computing
• neuromorphic computing
• in-memory computing
• energy-efficient AI
• system-level optimization