Threshold logic is an alternative representation of conventional Boolean logic and re-attracted researchers’ attention in recent years. Previous works have demonstrated that a 2 nd -order threshold logic gate (2-TLG) could have a lower area cost than a 1 st -order TLG (1-TLG) and proposed an integer linear programming (ILP)-based method for identifying 2-TLGs. However, the method could suffer from inefficiency for complex Boolean functions. In this article, we first enhance the ILP-based method for transforming a 1-TLG into a 2-TLG with a lower area cost. We observe that for a 2-TLG, most of the 2 nd -order weights (2-weights) are zero. That is, in the ILP formulation, most of the variables for the 2-weights can be set to zero without quality sacrifice. Thus, to identify the 2-weights that are more likely to be non-zero, we first propose sufficient conditions to derive a 2-TLG from a 1-TLG by extracting 2-weights. We then simplify the ILP formulation by eliminating the non-extracted 2-weights to facilitate the ILP-solving process. Furthermore, we propose a synthesis scheme for 2 nd -order threshold logic based on the enhanced ILP-based method. We leverage the state-of-the-art 1 st -order threshold logic synthesis technique to generate a 1 st -order threshold logic network (1-TLN) first and then transform it into a 2 nd -order TLN (2-TLN). The experimental results demonstrate that when transforming a set of 1-TLGs into 2-TLGs, the enhanced ILP-based method reduces the total CPU time by approximately 31% across all 1-TLGs, with only an average quality loss of 0.07% in terms of the area cost reduction rate. Additionally, when transforming two sets of 1-TLNs with different maximum fanin counts into 2-TLNs, the proposed method achieves average area cost reductions of 8.04% and 22.18%, respectively.

Deterministic database systems have been shown to significantly improve the availability and scalability of a distributed database system deployed on a shared-nothing architecture across WAN while ensuring strong consistency. However, their scalability and performance advantages highly depend on the quality of data partitioning due to the reduced flexibility in transaction processing. Although a deterministic database system can employ workload driven data (re-)partitioning and live data migration algorithms to partition data, we found that the effectiveness of these algorithms is limited in complex real-world environments due to the unpredictability of machine workloads. In this paper, we present Hermes, a deterministic database system prototype that, for the first time, does not rely on sophisticated data partitioning to achieve high scalability and performance. Hermes employs a novel transaction routing mechanism that jointly optimizes the balance of machine workloads, data (re-)partitioning, and live data migration by looking into the queued transactions to be executed in the near future. We conducted extensive experiments which show that Hermes is able to yield 29% to 137% increase in transaction throughput as compared to the state-of-the-art systems under complex real-world workloads.

Recent deterministic database systems have achieved high scalability and high availability in distributed environments given OLTP workloads. However, modern OLTP applications usually have changing workloads or access patterns, so how to make the resource provisioning elastic to the changing workloads becomes an important design goal for a deterministic database system. Live migration, which moves the specified data from a source machine to a destination node while continuously serving the incoming transactions, is a key technique required for the elasticity. In this paper, we present MgCrab, a live migration technique for a deterministic database system, that leverages the determinism to maintain the consistency of data on the source and destination nodes at very low cost during a migration period. We implement MgCrab on an open-source database system. Extensive experiments were conducted and the results demonstrate the effectiveness of MgCrab.

Epilepsy is a common neural disorder disease; about 0.5% of the global population has epilepsy [1]. Most patients take antiepileptic drugs to reduce their seizures. Among them, nearly one-third of the patients are drug-resistant epilepsy. The alternative treatment is the resection surgery of removing the epileptogenic zone. However, all above patients will still suffer seizures occasionally, which will influence the patients' quality-of-life, and further introduce danger and inconvenience to patients and people around. This paper presents the design and development of the smart headband for epileptic seizure detection. The headband consists of a fabric headband with flexible print circuit (FPC) inside and fabric electrodes on it. The whole system includes the following circuits: an analog front-end circuitry, an epileptic seizure detection tag (ESDT), and a Bluetooth Low Power (BLE) chip. The result of designed circuits yields a compact and low-power design of smart headband for epileptic seizure detection which is suitable for wearable usage. The epileptic seizure detection algorithm is validated by Children's Hospital Boston-MIT (CHB-MIT) EEG database [2].

Deterministic database systems have been shown to yield high throughput on a cluster of commodity machines while ensuring the strong consistency between replicas, provided that the data can be well-partitioned on these machines. However, data partitioning can be suboptimal for many reasons in real-world applications. In this paper, we present T-Part, a transaction execution engine that partitions transactions in a deterministic database system to deal with the unforeseeable workloads or workloads whose data are hard to partition. By modeling the dependency between transactions as a T-graph and continuously partitioning that graph, T-Part allows each transaction to know which later transactions on other machines will read its writes so that it can push forward the writes to those later transactions immediately after committing. This forward-pushing reduces the chance that the later transactions stall due to the unavailability of remote data. We implement a prototype for T-Part. Extensive experiments are conducted and the results demonstrate the effectiveness of T-Part.