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In this work, we present a new approach to high level synthesis (HLS), where high level functions are first mapped to an architectural template, before hardware synthesis is performed. As FPGA platforms are especially suitable for implementing streaming processing pipelines, we perform transformations on conventional high level programs where they are turned into multi-stage dataflow engines [1]. This target template naturally overlaps slow memory data accesses with computations and therefore has much better tolerance towards memory subsystem latency. Using a state-of-the-art HLS tool for the actual circuit generation, we observe up to 9x improvement in overall performance when the dataflow architectural template is used as an intermediate compilation target.
High-Level Synthesis (HLS) frameworks allow to easily specify a large number of variants of the same hardware design by only acting on optimization directives. Nonetheless, the hardware synthesis of implementations for all possible combinations of di
The globalization of the electronics supply chain is requiring effective methods to thwart reverse engineering and IP theft. Logic locking is a promising solution but there are still several open concerns. Even when applied at high level of abstracti
Accelerating tensor applications on spatial architectures provides high performance and energy-efficiency, but requires accurate performance models for evaluating various dataflow alternatives. Such modeling relies on the notation of tensor dataflow
Graph neural networks (GNN) represent an emerging line of deep learning models that operate on graph structures. It is becoming more and more popular due to its high accuracy achieved in many graph-related tasks. However, GNN is not as well understoo
We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph. A dialogue agent maps each user utterance to a program that extends this graph. Programs include metacomputation operators for reference and