Shared Frontend for Manycore Server Processors

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Modern hardware is increasingly complex, requiring increasing effort to understand in order to carefully engineer systems for optimal performance and effective utilization. Moreover, established design principles and assumptions are not portable to modern ...

Dataflow programming is a methodology that enables the development of high-level, parametric programs that are independent of the underlying platform. This approach is particularly useful for heterogeneous platforms, as it eliminates the need to rewrite ap ...

Advances in computational capabilities and large volumes of experimental data have established computer simulations of brain tissue models as an important pillar in modern neuroscience. Alongside, a variety of domain specific languages (DSLs) have been dev ...

By supporting the access of multiple memory words at the same time, Bit-line Computing (BC) architectures allow the parallel execution of bit-wise operations in-memory. At the array periphery, arithmetic operations are then derived with little additional o ...

Precise exceptions are a cornerstone of modern computing as they provide the abstraction of sequential instruction execution to programmers while accommodating microarchitectural optimizations. However, increasing compute capabilities in deep memory hierar ...

Modern data center fabrics open the possibility of microsecond distributed applications, such as data stores and message queues. A challenging aspect of their development is to ensure that, besides being fast in the common case, these applications react fa ...

Code-reuse attacks are dangerous threats that attracted the attention of the security community for years. These attacks aim at corrupting important control-flow transfers for taking control of a process without injecting code. Nowadays, the combinations o ...

Context. A novel high-performance exact pair-counting toolkit called fast correlation function calculator (FCFC) is presented.Aims. With the rapid growth of modern cosmological datasets, the evaluation of correlation functions with observational and simula ...

Modern programs are monolithic, combining code of varied provenance without isolation, all the while running on network-connected devices. A vulnerability in any component may compromise code and data of all other components. Compartmentalization separates ...

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an N-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network ...

Shared Frontend for Manycore Server Processors

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Chaosity: Understanding Contemporary NUMA-architectures

Design Space Exploration for Partitioning Dataflow Program on CPU-GPU Heterogeneous System

MOD2IR: High-Performance Code Generation for a Biophysically Detailed Neuronal Simulation DSL

Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference

Imprecise Store Exceptions

uKharon: A Membership Service for Microsecond Applications

WarpAttack: Bypassing CFI through Compiler-Introduced Double-Fetches

Fast correlation function calculator A high-performance pair-counting toolkit

SecureCells: A Secure Compartmentalized Architecture

POSEIDON: Privacy-Preserving Federated Neural Network Learning