Can Hardware Reconfigure Itself in Real Time? NextSilicon’s Adaptive Compute

2026-05-22 · Source: Big Data & AI News - EE Times · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Software Development & Engineering · Depth: Advanced, extended

Summary

NextSilicon introduces a runtime-reconfigurable processor architecture designed to adapt hardware to software, rather than the reverse. This novel approach, detailed by CEO Elad Raz, utilizes a "sea of ALUs" and real-time telemetry to optimize execution for specific code segments, removing 98% of the overhead found in traditional CPUs and GPUs. The company's Maverick 2 chip, a dataflow processor, dynamically reconfigures its ALUs based on application learning, addressing the limitations of static compilation and the high friction of manual hardware optimization. NextSilicon also developed its own RISC-V host CPU to handle serial code, allowing the dataflow accelerator to focus on parallelizable tasks and support diverse workloads, including HPC and AI, with a focus on general-purpose flexibility over specialized, fixed-function designs.

Key takeaway

For AI architects and HPC engineers evaluating next-generation compute platforms, NextSilicon's adaptive compute offers a compelling alternative to fixed-function accelerators. You should consider how runtime reconfigurability could reduce optimization friction and improve performance for diverse, evolving workloads, especially where code portability and long-term maintainability are critical. Explore its potential for both legacy code and new AI models, recognizing its strength in data-intensive, parallelizable tasks.

Key insights

Runtime-reconfigurable hardware adapts to code via telemetry, optimizing performance dynamically.

Principles

• Hardware should adapt to code, not vice-versa.
• Static compilation struggles with dynamic code behavior.
• General-purpose compute architectures ultimately prevail.

Method

NextSilicon's architecture uses a "sea of ALUs" and runtime telemetry to learn application behavior and dynamically reconfigure the dataflow graph on the silicon, optimizing performance on the fly.

In practice

• Combine dataflow accelerators with host CPUs for mixed workloads.
• Utilize runtime telemetry to specialize kernels for specific data patterns.
• Target bottlenecks by optimizing data streaming within compute graphs.

Topics

• Adaptive Compute
• Runtime Reconfiguration
• Dataflow Architecture
• RISC-V CPU
• High Performance Computing
• AI Accelerators
• Hardware Optimization

Best for: AI Engineer, AI Hardware Engineer, AI Architect, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Big Data & AI News - EE Times.