In this paper, we present a complete framework for celltype selection in modern high-performance low-power designs with library-based timing model. Our framework can be divided into three stages. First, the best design performance with all possible cell-types is achieved by a Minimum Clock Period Lagrangian Relaxation (MinClock LR) method, which extends the traditional LR approach to conquer the difficulties in discrete scenario. Min-Clock LR fully leverages the prevalent many-core systems as the main body of its workload is composed of independent tasks. Upon a timing-valid design, we solve the timing-constrained power optimization problem by min-cost network flow. Especially, we identify and address the core issues in applying network flow technique to library-based timing model. Finally, a power prune technique is developed to take advantage of the residual slacks due to the conservative network flow construction. Experiments on ISPD 2012 benchmarks show that on average we can save 13% more leakage power on designs with fast timing constraints compared to start-of-theart techniques. Moreover, our algorithm shows a linear empirical runtime , finishing the largest benchmark with one million cells in 1.5 hours.

No abstract

No abstract

In this paper, we propose an enhanced quasimaximum likelihood (EQML) decoder for LDPC codes with short block lengths. After the failure of the conventional belief propagation (BP) decoding, the proposed EQML decoder selects unreliable variable nodes (VNs) and saturates their associated channel output values to generate a list of decoder input sequences. Each decoder input sequence in the list is then decoded by the conventional BP decoder to obtain the most likely codeword. To improve the accuracy of selecting unreliable VNs, we propose an edge-wise selection method based on the sign fluctuation of VNs' extrinsic messages. A partial pruning stopping (PPS) rule is also presented to reduce the decoding latency. Simulation results show that the proposed EQML decoder outperforms the conventional BP decoder and the augmented BP decoder for short LDPC codes. It even approaches the performance of ML decoding within 0.3 dB in terms of frame error rate. In addition, the proposed PPS rule achieves a lower decoding latency compared to the list decoding stopping rule.

In this letter, we propose a reliability-based windowed decoding scheme for spatially-coupled (SC) low-density parity-check (LDPC) codes. To mitigate the error propagation along the sliding windowed decoder of the SC LDPC codes, a partial message reservation (PMR) method is proposed where only the reliable messages generated in the previous decoding window are reserved for the next decoding window. We also propose a partial syndrome check (PSC) stopping rule for each decoding window, in which only the complete VNs are checked. Simulation results show that our proposed scheme significantly improves the error floor performance compared to the sliding windowed decoder with the conventional weighted bit-flipping (WBF) algorithm.

No abstract

The sense of touch is essential for a variety of daily tasks. New advances in event-based tactile sensors and Spiking Neural Networks (SNNs) spur the research in event-driven tactile learning. However, SNN-enabled event-driven tactile learning is still in its infancy due to the limited representative abilities of existing spiking neurons and high spatio-temporal complexity in the data. In this paper, to improve the representative capabilities of existing spiking neurons, we propose a novel neuron model called "location spiking neuron", which enables us to extract features of event-based data in a novel way. Moreover, based on the classical Time Spike Response Model (TSRM), we develop a specific location spiking neuron model -Location Spike Response Model (LSRM) that serves as a new building block of SNNs 1 . Furthermore, we propose a hybrid model which combines an SNN with TSRM neurons and an SNN with LSRM neurons to capture the complex spatio-temporal dependencies in the data. Extensive experiments demonstrate the significant improvements of our models over other works on event-driven tactile learning and show the superior energy efficiency of our models and location spiking neurons, which may unlock their potential on neuromorphic hardware. 2

No abstract