Performance analysis of Network-on-Chips(NoC) plays a crucial role in design space exploration of SoCs, but traditional cycle-accurate NoC simulation often limits the ability to explore a large design space efficiently due to their notoriously slow execution. There exist several lightweight performance analysis techniques to reduce the design-space exploration time for NoCs, but all of them lack flexibility. In this work, we present FlexNoC - an end-to-end fast and flexible NoC performance analysis framework based on analytical modeling grounded on queuing theory. FlexNoC considers NoCs with irregular topologies and hybrid arbitration which no existing NoC performance analysis framework considers. We establish a Domain-Specific Language (DSL) to describe an NoC with any topology. Specifically, we extend the DOT language using ANTLR-based grammar to support custom NoC primitives such as injectors, queues, servers, arbiters, sinks, and splits. The DSL enables user-defined network components and their interconnections. The queuing theory based analytical model which is the backbone of the framework incorporates hybrid arbitration, along with finite buffers to accurately capture complex interactions between queues present in any given NoC. FlexNoC is accurate in NoC performance estimation and three orders of magnitude faster than cycle accurate NoC simulation - offering an efficient platform for rapid design space exploration and early-stage NoC performance optimization. Moreover, we demonstrate that FlexNoC, through rapid design space exploration, unlocks new insights regarding arbitration techniques at router ports.

Digital Microfluidic Biochips (DMFBs) have the potential to fundamentally transform biochemical disciplines through automation, miniaturization, and the ability to facilitate repeatable chemical experimentation. Programming DMFBs has historically been accomplished by writing low-level bit manipulations to select which electrodes should activate in sequence. Recent research on high-level programming languages and compilers for DMFBs have begun to address the programmability challenge, but important capabilities such as loading and executing pre-compiled libraries and function calls, are absent from the literature. A primary driver of this oversight is the lack of a memory hierarchy to store physical chemicals off-chip to jump to and from function calls. This paper addresses the complexities involved in compiling function calls within the technology's unique boundaries, and provides a proof-of-concept implementation from language to code generation, with solutions evaluated using a cycle-accurate DMFB simulator as well as physical execution on an open-hardware DMFB.

This paper introduces a compiler optimization strategy for Software-Programmable Laboratories-on-a-Chip (SP-LoCs), which miniaturize and automate a wide variety of benchtop laboratory experiments. The compiler targets a specific class of SP-LoCs that manipulate discrete liquid droplets on a 2D grid, with cyber-physical feedback provided by integrated sensors and/or video monitoring equipment. The optimization strategy employed here aims to reduce the overhead of transporting fluids between operations, and explores tradeoffs between the latency and resource requirements of mixing operations: allocating more space for mixing shortens mixing time, but reduces the amount of spatial parallelism available to other operations. The compiler is empirically evaluated using a cycle-accurate simulator that mimics the behavior of the target SP-LoC. Our results show that a coalescing strategy, inspired by graph coloring register allocation, effectively reduces droplet transport latencies while speeding up the compiler and reducing its memory footprint. For biochemical reactions that are dominated by mixing operations, we observe a linear correlation between a preliminary result using a default mixing operation resource allocation and the percentage decrease in execution time that is achieved via resizing.

This paper introduces BioScript, a domain-specific language (DSL) for programmable biochemistry which executes on emerging microfluidic platforms. The goal of this research is to provide a simple, intuitive, and type-safe DSL that is accessible to life science practitioners. The novel feature of the language is its syntax, which aims to optimize human readability; the technical contributions of the paper include the BioScript type system and relevant portions of its compiler. The type system ensures that certain types of errors, specific to biochemistry, do not occur, including the interaction of chemicals that may be unsafe. The compiler includes novel optimizations that place biochemical operations to execute concurrently on a spatial 2D array platform on the granularity of a control flow graph, as opposed to individual basic blocks. Results are obtained using both a cycle-accurate microfluidic simulator and a software interface to a real-world platform.