Related papers: Transparent hardware synthesis of Java for predict…

In applications of machine learning to particle physics, a persistent challenge is how to go beyond discrimination to learn about the underlying physics. To this end, a powerful tool would be a framework for unsupervised learning, where the…

Generalized sampling is a numerically stable framework for obtaining reconstructions of signals in different bases and frames from their samples. In this paper, we will introduce a carefully documented toolbox for performing generalized…

We introduce JPC, a JAX library for training neural networks with Predictive Coding. JPC provides a simple, fast and flexible interface to train a variety of PC networks (PCNs) including discriminative, generative and hybrid models. Unlike…

Generative large language models (LLMs) have garnered significant attention due to their exceptional capabilities in various AI tasks. Traditionally deployed in cloud datacenters, LLMs are now increasingly moving towards more accessible…

Embarrassingly parallel problems can be split in parts that are characterized by a really low (or sometime absent) exchange of information during their computation in parallel. As a consequence they can be effectively computed in parallel…

Distributed applications often deal with data with different consistency requirements: while a post in a social network only requires weak consistency, the user balance in turn has strong correctness requirements, demanding mutations to be…

Graphics Processing Units (GPUs) are deployed on most present server, desktop, and even mobile platforms. Nowadays, a growing number of applications leverage the high parallelism offered by this architecture to speed-up general purpose…

AI acceleration has been dominated by GPUs, but the growing need for lower latency, energy efficiency, and fine-grained hardware control exposes the limits of fixed architectures. In this context, Field-Programmable Gate Arrays (FPGAs)…

Parallel data processing has become indispensable for processing applications involving huge data sets. This brings into focus the Graphics Processing Units (GPUs) which emphasize on many-core computing. With the advent of General Purpose…

Reverse Engineering(RE) has been a fundamental task in software engineering. However, most of the traditional Java reverse engineering tools are strictly rule defined, thus are not fault-tolerant, which pose serious problem when noise and…

This paper proposes a framework for automatic formal controller synthesis for general hybrid systems with a subset of safety and reachability specifications. The framework uses genetic programming to automatically co-synthesize controllers…

BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation. It is designed for ease of use, speed, and modularity by taking a functional approach to the algorithms' implementation.…

Many modern virtual machines, such as JVMs, .NET Framework, and V8, employ a just-in-time (JIT) compiler to achieve their high-performance. There are two major compilation strategies; trace-based compilation and method-based compilation.…

We proposes a platform which can generate hardware/software description based on flexible in-struction set architectures (ISAs). The platform takes advantage of the flexibility of field pro-grammable gate array (FPGA) to design many micro…

We present JAX-PF, an open-source, GPU-accelerated, and differentiable Phase Field (PF) software package, supporting both explicit and implicit time stepping schemes. Leveraging the modern computing architecture JAX, JAX-PF achieves high…

To amortize cost, cloud vendors providing DNN acceleration as a service to end-users employ consolidation and virtualization to share the underlying resources among multiple DNN service requests. This paper makes a case for a "preemptible"…

In recent years, Field-Programmable Gate Arrays (FPGA) have evolved rapidly paving the way for a whole new range of computing paradigms. On the other hand, computer applications are evolving. There is a rising demand for a system that is…

Technical computing is a challenging application area for programming languages to address. This is evinced by the unusually large number of specialized languages in the area (e.g. MATLAB, R), and the complexity of common software stacks,…

General purpose CPUs used in high performance computing (HPC) support a vector instruction set and an out-of-order engine dedicated to increase the instruction level parallelism. Hence, related optimizations are currently critical to…

We present a scalable, robust approach to creating quantum programs of arbitrary size and complexity. The approach is based on the true abstraction of the problem. The quantum program is expressed in terms of a high-level model together…