OpenDF – A Dataﬂow Toolset for Reconﬁgurable Hardware and Multicore Systems

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State-of-the-art Artificial Intelligence (AI) algorithms, such as graph neural networks and recommendation systems, require floating-point computation of very large matrix multiplications over sparse data. Their execution in resource-constrained scenarios, ...

We identified that in modern commercial FPGAs, routing signals from the general interconnect to the configurable logic blocks (CLBs) through a very sparse input interconnect block (IIB) represents a significant runtime bottleneck. This is despite academic ...

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 ...

Interactive mobile applications like web browsing and gaming are known to benefit significantly from low latency networking, as applications communicate with cloud servers and other users' devices. Emerging mobile channel standards have not met these needs ...

The performance of programs executed on heterogeneous parallel platforms largely depends on the design choices regarding how to partition the processing on the various different processing units. In other words, it depends on the assumptions and parameters ...

The hardware complexity of modern machines makes the design of adequate programming models crucial for jointly ensuring performance, portability, and productivity in high-performance computing (HPC). Sequential task-based programming models paired with adv ...

Recent GPU architectures make available numbers of parallel processing units that exceed by orders of magnitude the ones offered by CPU architectures. Whereas programs written using dataflow programming languages are well suited for programming heterogeneo ...

The performance of programs written in languages following the dataflow model of computation (MoC) largely depends on the configuration (partitioning, mapping, scheduling, buffer dimensioning) chosen during the synthesis stages. Furthermore, this programmi ...

Writing and optimizing application software for heterogeneous platforms including GPU units is a very difficult task that requires designer efforts and resources to consider several key elements to obtain good performance. Dataflow programming has shown to ...

The ability of dataflow circuits to implement dynamic scheduling promises to overcome the conservatism of static scheduling techniques that high-level synthesis tools typically rely on. Yet, the same distributed control mechanism that allows dataflow circu ...

High-level synthesis (HLS) tools typically generate statically scheduled datapaths. Static scheduling implies that the resulting circuits have a hard time exploiting parallelism in code with potential memory dependences, with control dependences, or where ...

OpenDF – A Dataﬂow Toolset for Reconﬁgurable Hardware and Multicore Systems

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A 16-bit Floating-Point Near-SRAM Architecture for Low-power Sparse Matrix-Vector Multiplication

IIBLAST: Speeding Up Commercial FPGA Routing by Decoupling and Mitigating the Intra-CLB Bottleneck

Imprecise Store Exceptions

DChannel: Accelerating Mobile ApplicationsWith Parallel High-bandwidth and Low-latency Channels

Performance Estimation of High-Level Dataflow Program on Heterogeneous Platforms by Dynamic Network Execution

Decentralized in-order execution of a sequential task-based code for shared-memory architectures

Inter-actions parallel execution on GPU from high-level dataflow synthesis

Performance Estimation of High-Level Dataflow Program on Heterogeneous Platforms

SIMD Parallel Execution on GPU from High-Level Dataflow Synthesis

A Comprehensive Timing Model for Accurate Frequency Tuning in Dataflow Circuits

From C/C plus plus Code to High-Performance Dataflow Circuits