Better Hardware Could Turn Zeros into AI Heroes

2026-04-28 · Source: IEEE Spectrum · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, AI Hardware · Depth: Expert, long

Summary

Stanford University researchers have developed Onyx, a novel hardware accelerator designed to efficiently process both sparse and dense computations in AI models. As large language models (LLMs) like Meta's Llama 4, with 2 trillion parameters, continue to grow, their energy demands and computational times increase, leading to higher carbon footprints. Onyx addresses this by exploiting sparsity, a property where many model parameters are zero or near-zero, allowing these calculations to be skipped and memory storage reduced. Unlike current multicore CPUs and GPUs, which are not optimized for unstructured sparsity, Onyx is a programmable coarse-grained reconfigurable array (CGRA) that re-architects the entire design stack, including hardware, firmware, and software. It achieves up to 565 times better energy-delay product compared to CPUs using sparse libraries, consuming one-seventieth the energy and performing computations eight times faster on average.

Key takeaway

For research scientists developing or deploying large AI models, understanding and implementing sparsity is critical for managing escalating computational costs and environmental impact. Your current hardware, like GPUs, may not fully exploit unstructured sparsity, leading to wasted energy. Explore dedicated sparse computing architectures like Onyx to achieve substantial gains in energy efficiency and speed, enabling the development of new, more performant algorithms.

Key insights

Sparsity in AI models offers significant computational and energy savings when supported by purpose-built hardware.

Principles

• Sparsity can be natural or induced.
• Compressing zeros saves memory and energy.
• Skipping zero operations reduces computation.

Method

Onyx, a CGRA, maps abstract memory and compute nodes from input expressions onto flexible processing and memory tiles, then routes them to transfer data, configured by a compiler for sparse or dense operations.

In practice

• Induce sparsity in LLMs (e.g., 70-80% zero parameters).
• Explore new algorithms leveraging sparsity.
• Consider Onyx-like architectures for energy-efficient AI.

Topics

• Sparsity
• Hardware Acceleration
• Onyx Accelerator
• Large Language Models
• Coarse-Grained Reconfigurable Arrays

Best for: Research Scientist, AI Hardware Engineer, AI Scientist, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by IEEE Spectrum.