MIDDLEWARE

Deep Neural Networks (DNNs) have demonstrated impressive performance on many machine-learning tasks such as image recognition and language modeling, and are becoming prevalent even on mobile platforms. Despite so, designing neural architectures still ...

As more and more mobile/embedded applications employ Deep Neural Networks (DNNs) involving sensitive user data, mobile/embedded devices must provide a highly secure DNN execution environment to prevent privacy leaks. Aimed at securing DNN data, recent ...

Modern software programs have dedicated license-check modules that restrict access to users, who possess valid credentials. They also have a large number of add-on pluggable modules that can be separately purchased and have their dedicated license ...

For resource utilization efficiency, latency critical (LC) services are commonly co-located with best-effort batch jobs in datacenter servers. Many LC services, such as Cassandra and HBase, run in Java Virtual Machine (JVM). We find that LC services ...

Byzantine fault-tolerant agreement (BFT) in a partially synchronous system usually requires 3f + 1 nodes to tolerate f faulty replicas. Due to their high throughput and finality property, BFT algorithms build the core of recent permissioned blockchains. ...

As private space companies such as SpaceX and Telesat are building large LEO satellite constellations to provide global broadband Internet access, researchers have proposed to embed compute services within satellite constellations to provide computing ...

In recent years, ROS becomes the dominant middleware for robotic systems. The performance of its message-passing paradigm is crucial to the robot's reaction time. However, previous works only focus on efficiency, but ignore the requirement for ...

Random forests use ensembles of decision trees to boost accuracy for machine learning tasks. However, large ensembles slow down inference on platforms that process each tree in an ensemble individually. We present Bolt, a platform that restructures whole ...

Federated Learning (FL) is a popular deep learning approach that prevents centralizing large amounts of data, and instead relies on clients that update a global model using their local datasets. Classical FL algorithms use a central federator that, for ...

Confidential computing services enable users to run their workloads in Trusted Execution Environments (TEEs) leveraging secure hardware like Intel SGX, and verify them by performing remote attestation. This process offers necessary proof for the ...

Machine Learning (ML) has emerged as a core technology to provide learning models to perform complex tasks. Boosted by Machine Learning as a Service (MLaaS), the number of applications relying on ML capabilities is ever increasing. However, ML models are ...

Serverless platforms offer high resource elasticity and pay-as-you-go billing, making them a compelling choice for data analytics. To craft a "pure" serverless solution, the common practice is to transfer intermediate data between serverless functions ...

Low-power operating system runtimes used on IoT microcontrollers typically provide rudimentary APIs, basic connectivity and, sometimes, a (secure) firmware update mechanism. In contrast, on less constrained hardware, networked software has entered the ...

The number of fake news written by bots or malicious actors on social media is rising. One cause is the ability of users to post anything, at any place, at any time. This offers great flexibility, but it also poses the risk that users share ...

Federated learning (FL) is a machine learning paradigm that enables a cluster of decentralized edge devices to collaboratively train a shared machine learning model without exposing users' raw data. However, the intensive model training computation is ...

Using a flash-based layer to serve the caching and buffering needs of multiple workloads has become a common practice. In such settings, resource demands will inevitably exceed available capacity sometimes. "Fair" resource allocation may offer a ...

Raw flash file systems are essential in today's cost-sensitive embedded systems and legacy embedded devices. The performance of raw flash file systems is often limited by their inefficient garbage collection (GC) due to the tight capacity of the onboard ...

Autotuning DNN models prior to their deployment is an essential but time-consuming task. Using expensive (and power-hungry) GPU and TPU accelerators efficiently is also key. Since DNNs do not always use a GPU fully, spatial multiplexing of multiple ...

The ability to scale out training workloads has been one of the key performance enablers of deep learning. The main scaling approach is data-parallel GPU-based training, which has been boosted by hardware and software support for highly efficient point-...

Deep Reinforcement Learning (DRL) achieves great success in various domains. Communication in today's DRL algorithms takes non-negligible time compared to the computation. However, prior DRL frameworks usually focus on computation management while paying ...

Light clients have been widely used in blockchain systems to support lightweight nodes by synchronizing and verifying block headers only. However, there are two major limitations with the current light client design. First, with the ever increasing ...

Mission-critical, real-time, continuous stream processing applications that interact with the real world have stringent latency requirements. For example, e-commerce websites like Amazon improve their marketing strategy by performing real-time ...

Conflict-free replicated data types (CRDTs) are popular for optimistic replication and ensuring strong eventual consistency (SEC) in distributed systems. However, reversibility is an underdeveloped functionality for CRDTs, despite its usefulness in ...

Genome-wide association studies (GWAS) identify correlations between the genetic variants and an observable characteristic such as a disease. Previous works presented privacy-preserving distributed algorithms for a federation of genome data holders that ...

With the proliferation of high bandwidth cameras and AR/VR devices, and their increasing use in situation awareness applications, edge computing is gaining prominence to meet the throughput requirements of such applications. This work focuses on camera ...

Federated Learning (FL) opens new perspectives for training machine learning models while keeping personal data on the users premises. Specifically, in FL, models are trained on the users' devices and only model updates (i.e., gradients) are sent to a ...