Evaluating inference latency for specialized silicon at the network edge versus centralized cloud deployments.
Karan Talwar
Embedded Systems Engineer
Running deep learning networks on localized hardware reduces network costs and latency, but introduces compute bottlenecks. This benchmark evaluates edge TPUs, NPUs, and GPUs against classic cloud servers.
Edge silicon provides surprisingly fast execution for quantized models (INT8). By avoiding round-trip times to remote cloud centers, edge-side inference achieves sub-10ms latency for vision tasks.
Embedded Systems Engineer
Karan designs firmware and compiles machine learning models for low-power edge accelerators.
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