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Quantization is considered as one of the most effective methods to optimize the inference cost of neural network models for their deployment to mobile and embedded systems, which have tight resource constraints. In such approaches, it is critical to provide low-cost quantization under a tight accuracy loss constraint (e.g., 1%). In this paper, we propose a novel method for quantizing weights and activations...
We propose a new timing error correction scheme for area-efficient design of flip-flop based pipeline. Key features in the proposed scheme are 1) one-cycle error correction using a new local stalling scheme and 2) selective replacement of the error detection and correction flip-flops in critical paths only. A 32-bit MIPS testchip in a 65 nm CMOS technology has been implemented as a testbed. By employing...
Graph computation is becoming more and more popular in machine learning, big data analytics, etc. For such workloads, GPU is considered as an efficient execution platform since graph computation is characterized by massively parallel computation and high demand of memory bandwidth. In our investigation, existing GPU programming methods for graph computation do not fully exploit high memory bandwidth...
Deep neural networks (DNNs) have recently proved their effectiveness in complex data analyses such as object/speech recognition. As their applications are being expanded to mobile devices, their energy efficiencies are becoming critical. In this paper, we propose a novel concept called big/LITTLE DNN (BL-DNN) which significantly reduces energy consumption required for DNN execution at a negligible...
Big data processing, e.g., graph computation and MapReduce, is characterized by massive parallelism in computation and a large amount of fine-grained random memory accesses often with structural localities due to graph-like data dependency. Recently, GPU is gaining more and more attention for servers due to its capability of parallel computation. However, the current GPU architecture is not well suited...
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