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Explicit Sign-Magnitude Encoders Enable Power-Efficient Multipliers
Felix Arnold, Maxence Bouvier, Ryan Amaudruz, Renzo Andri, Lukas Cavigelli
https://arxiv.org/abs/2507.18179 h…

Explicit Sign-Magnitude Encoders Enable Power-Efficient Multipliers
This work presents a method to maximize power-efficiency of fixed point multiplier units by decomposing them into sub-components. First, an encoder block converts the operands from a two's complement to a sign magnitude representation, followed by a multiplier module which performs the compute operation and outputs the resulting value in the original format. This allows to leverage the power-efficiency of the Sign Magnitude encoding for the multiplication. To ensure the computing format is not …

ECLIP: Energy-efficient and Practical Co-Location of ML Inference on Spatially Partitioned GPUs
Ryan Quach, Yidi Wang, Ali Jahanshahi, Daniel Wong, Hyoseung Kim
https://arxiv.org/abs/2506.12598

ECLIP: Energy-efficient and Practical Co-Location of ML Inference on Spatially Partitioned GPUs
As AI inference becomes mainstream, research has begun to focus on improving the energy consumption of inference servers. Inference kernels commonly underutilize a GPU's compute resources and waste power from idling components. To improve utilization and energy efficiency, multiple models can co-locate and share the GPU. However, typical GPU spatial partitioning techniques often experience significant overheads when reconfiguring spatial partitions, which can waste additional energy through rep…