Streaming data programs are an important class of applications, for which queueing network models are frequently available. While the design space can be large, decomposition techniques can be effective at design space reduction. We introduce two decomposition techniques called convex decomposition and unchaining and present implications for a biosequence search application.

Streaming data applications are frequently pipelined and deployed on application-specific systems to meet performance requirements and resource constraints. Typically, there are several design parameters in the algorithms and architectures used that impact the application performance as well as resource utilization. Efficient exploration of this design space is the goal of this research. When using architecturally diverse systems to accelerate streaming applications, the design search space is often complex. The search complexity can be reduced by recognizing and exploiting convex variables to perform convex decomposition, preserving optimality even in the context of a non-convex optimization problem. This paper presents a formal treatment of convex variables and convex decomposition, including a proof that the technique preserves optimality. It also quantifies the reduction in the search space that is realized, at minimum equal to the number of distinct values of the convex variable and potentially much higher.

Many embedded and scientific applications are pipelined (i.e., streaming) and deployed on application-specific systems. Typically, there are several design parameters in the algorithms and architectures used that impact the tradeoff between different metrics of application performance as well as resource utilization. Efficient automatic exploration of this design space is the goal of our research. We present a global optimization framework comprising a domain-specific variation of branch-and-bound that reduces search complexity by exploiting the topology of the application's pipelining. We exploit the topological information to discover decomposability through the canonical Jordan block form. The reduction in search complexity for four real-world streaming applications (drawn from the literature) is significant, ranging from a million-fold reduction in search space size to a reduction factor of 10 billion. All four optimization problems are thereby solvable in reasonable time.

Non-hierarchical k-means algorithms have been implemented in hardware, most frequently for image clustering. Here, we focus on hierarchical clustering of text documents based on document similarity. To our knowledge, this is the first work to present a hierarchical clustering algorithm designed for hardware implementation and ours is the first hardware-accelerated implementation

Applications for constrained embedded systems are subject to strict time constraints and restrictive resource utilization. With soft core processors, application developers can customize the processor for their application, constrained by resources but aimed at high application performance. With such freedom in the design space of the processor, however, comes complexity. We present here an automatic optimization technique that helps the developers with the processor microarchitecture customization. A naive approach exploring all possible configurations is exponential with the number of parameters and hence is clearly infeasible, even with only tens of reconfigurable parameters. Instead, our approach runs in time that is linear with the number of parameter values, based on an assumption of parameter independence. This makes the approach feasible and scalable. For the dimensions that we customize, namely application runtime and hardware resources, we formulate their costs as a constrained binary integer nonlinear optimization program. Though the results are not guaranteed to be optimal, we find they are near-optimal in practice. Our technique itself is general and can be applied to other design-space exploration problems