Abstract We show how to use passive automata learning to infer models of attacker-defender interactions in cybersecurity. By treating system event logs as words in a formal language, we can apply algorithms such as RPNI to infer compact deterministic finite automata from observed traces. We evaluate this approach through a case study on the Cyber Operations Research Gymnasium (CybORG), a widely-used simulation framework for training defensive agents using machine learning. We analyze the structural properties of the inferred automata and assess their empirical fidelity with respect to the semantics of CybORG. Our results show that accurate formal models can be learned from a relatively small number of traces, suggesting a promising path toward more automated and data-driven approaches to cybersecurity.

Signal processing applications have been shown to map well to time multiplexed coarse grained reconfigurable array (CGRA) devices, and can often be decomposed into a set of communicating kernels. This decomposition can facilitate application development and reuse but has significant consequences for tools targeting these devices in terms of allocation and arrangement of resources. This paper presents a CGRA floorplanner to optimize the division and placement of resources for multi-kernel applications. The task is divided into two phases aligned with the respective goals. Resource allocation is accomplished through incremental assignment to minimize performance bottlenecks while operating within the bounds of the maximum available resources. The resulting allocation of resources is arranged in the device using simulated annealing and a perimeter-based cost function which serves to minimize resources needed for both interand intra-kernel communications. The floorplanner is applied to a set of multi-kernel benchmarks demonstrating resource allocations providing maximum throughput across a range of available resources. The algorithms are very fast, taking only a few seconds while producing high quality results. Inter-kernel wire lengths are almost always minimal, and the resource allocation is proven optimal.

In this paper we present SPR, a new architecture-adaptive mapping tool for use with Coarse-Grained Reconfigurable Architectures (CGRAs). It combines a VLIW style scheduler and FPGA style placement and pipelined routing algorithms with novel mechanisms for integrating and adapting the algorithms to CGRAs. We introduce a latency padding technique that provides feedback from the placer to the scheduler to meet the constraints of a fixed frequency device with configurable interconnect. Using a new dynamic clustering method during placement, we achieved a 1.3x improvement in throughput of mapped designs. Finally, we introduce an enhancement to the PathFinder algorithm for targeting architectures with a mix of dynamically multiplexed and statically configurable interconnects. The enhanced algorithm is able to successfully share statically configured interconnect in a time-multiplexed way, achieving an average channel width reduction of .5x compared to non-shared static interconnect.

Spatially-tiled architectures, such as coarse-grained reconfigurable arrays (CGRAs), are powerful architectures for accelerating applications in the digital-signal processing, embedded, and scientific computing domains. In contrast to field-programmable gate arrays (FPGAs), another common accelerator, they typically time-multiplex their processing elements and are word rather than bit-oriented. These differences lead us to re-examine some of the traditional architecture choices made for FPGAs as we move to these coarser-granularity architectures. In this paper we study the efficiency of time-multiplexing global interconnect as architectures scale from single-bit to multi-bit datapaths. Using the Mosaic infrastructure, we analyzed the design trade-offs involved in static vs. time-multiplexed routing for global interconnect channels, as well as the benefit of including a dedicated bit-wide control interconnect to supplement the word-wide datapath of a CGRA. We show that a time-multiplexed interconnect is beneficial in these coarse-grained systems, reducing the area-energy product to 0.32times the area-energy product of a fully static interconnect. We also show that for our benchmarks, which include single-bit control logic, providing both word and bit-wide interconnect resources further reduces the area-energy product to 0.94times that of an exclusively word-wide interconnect.

Platform FPGAs that integrate sequential processors with a spatial fabric have become prevalent. While these hybrid architectures ease the burden of integrating sequential and spatial code in a single application, programming them, and particularly their spatial fabrics remains challenging. The difficulty arises in part from the lack of an agreed upon computational model and family of programming languages. In addition, moving algorithms into hardware is an arcane art far removed from the experience of most programmers. To address this challenge, we present a new type architecture, an abstract model analogous to the von Neumann machine for sequential computers, that can serve as common ground for algorithm designers, language designers, and hardware architects. We show that many parallel architectures, including platform FPGAs, are implementations of this type architecture. Using examples from a variety of application domains, we show how algorithms can be analyzed to estimate their performance on implementations of this type architecture. This analysis is done without having to delve into the details of any architecture in particular. Finally, we describe some of the common features of languages designed for expressing micro-parallelism, highlighting connections with the type architecture

Programmable spatial fabrics, such as FPGAs, can provide some of the performance and efficiency benefits of custom hardware while retaining the low cost and flexibility of reprogrammable architectures. However, these fine-grained parallel architectures still have not been as widely adopted as many believe they could be for computationally intensive applications. The problem is two-fold: First, most applications contain substantial amounts of mostly sequential code that does not execute efficiently on a spatial fabric. Second, programming spatial architectures still requires some knowledge of the arcane arts of hardware engineering.Recently, hybrid processors that integrate a sequential processor with a spatial fabric have become prevalent. While hybrid computers ease the burden of integrating sequential and spatial code in a single application, programming them, and particularly their spatial fabrics, remains challenging. Part of the difficulty lies in the lack of a commonly agreed upon computational model and family of programming languages.To address this challenge, we are developing a new type architecture--an abstract model analogous to the von Neumann machine for sequential computers--that can serve as common ground for algorithm designers, language designers, and hardware architects. We show how this model applies to several relevant architectures, and present examples of how it can effectively inform algorithm, language, and hardware design, thereby improving the programmability of hybrid processors.

In this paper we hypothesize that reconfigurable computing is not more widely used because of the logistical difficulties caused by the close coupling of applications and hardware platforms. As an alternative, we propose computing machines that use a single, serial instruction representation for the entire reconfigurable computing application. We show how it is possible to convert, at runtime, the parallel portions of the application into a spatial representation suitable for execution on a reconfigurable fabric. The conversion to spatial representation is facilitated by the use of an instruction set architecture based on an operand queue. We describe techniques to generate code for queue machines and hardware virtualization techniques necessary to allow any application to execute on any platform.