Podcasts · The Hardware Podcast with Fexingo: Chips, Devices, and Electronics Engineering Conversations
How Chips Are Learning to Compress Data at the Source
· 12 min · S2 E63
Episode How Chips Are Learning to Compress Data at the Source
By 2026, the amount of data generated at the edge is overwhelming memory bandwidth and power budgets. Lucas explains how a technique called near-sensor compression is moving data reduction from the CPU to the sensor itself. He walks through a concrete example: a 4K image sensor that outputs 400 megabytes per second, and how a dedicated compression engine can shrink that by 60 percent with no visible quality loss. Luna asks about the trade-offs in latency and energy, and Lucas points to a 2025 paper from Samsung showing a 45 percent power reduction in an always-on camera pipeline. They also discuss how this approach is spreading to lidar, microphones, and even IoT temperature sensors. The episode closes with the question of whether near-sensor compression could become a standard IP block in every system-on-chip.
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Transcript
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