Name
Abstract | ||
|---|---|---|
Emerging embedded applications are based on evolving standards (e.g., MPEG2/4, H.264/265, IEEE802.11a/b/g/n). Since most of these applications run on handheld devices, there is an increasing need for a single chip solution that can dynamically interoperate between different standards and their derivatives. In order to achieve high resource utilization and low power dissipation, we propose REDEFINE, a polymorphic ASIC in which specialized hardware units are replaced with basic hardware units that can create the same functionality by runtime re-composition. It is a “future-proof” custom hardware solution for multiple applications and their derivatives in a domain. In this article, we describe a compiler framework and supporting hardware comprising compute, storage, and communication resources. Applications described in high-level language (e.g., C) are compiled into application substructures. For each application substructure, a set of compute elements on the hardware are interconnected during runtime to form a pattern that closely matches the communication pattern of that particular application. The advantage is that the bounded CEs are neither processor cores nor logic elements as in FPGAs. Hence, REDEFINE offers the power and performance advantage of an ASIC and the hardware reconfigurability and programmability of that of an FPGA/instruction set processor. In addition, the hardware supports custom instruction pipelining. Existing instruction-set extensible processors determine a sequence of instructions that repeatedly occur within the application to create custom instructions at design time to speed up the execution of this sequence. We extend this scheme further, where a kernel is compiled into custom instructions that bear strong producer-consumer relationship (and not limited to frequently occurring sequences of instructions). Custom instructions, realized as hardware compositions effected at runtime, allow several instances of the same to be active in parallel. A key distinguishing factor in majority of the emerging embedded applications is stream processing. To reduce the overheads of data transfer between custom instructions, direct communication paths are employed among custom instructions. In this article, we present the overview of the hardware-aware compiler framework, which determines the NoC-aware schedule of transports of the data exchanged between the custom instructions on the interconnect. The results for the FFT kernel indicate a 25% reduction in the number of loads/stores, and throughput improves by log(n) for n-point FFT when compared to sequential implementation. Overall, REDEFINE offers flexibility and a runtime reconfigurability at the expense of 1.16× in power and 8× in area when compared to an ASIC. REDEFINE implementation consumes 0.1× the power of an FPGA implementation. In addition, the configuration overhead of the FPGA implementation is 1,000× more than that of REDEFINE. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1596543.1596545 | ACM Trans. Embedded Comput. Syst. |
Keywords | Field | DocType |
custom instruction extension,custom hardware solution,noc,runtime reconfiguration,application synthesis,custom instruction pipelining,custom instruction,hardware reconfigurability,honeycomb,polymorphic asic,router,hardware composition,application substructure,specialized hardware unit,basic hardware unit,embedded application,dataflow software pipeline,fpga implementation,data exchange,high level language,chip,handheld device,resource utilization,polymorphism,stream processing,data transfer,software pipelining | Reconfigurability,Instruction set,Computer science,Real-time computing,Multi-core processor,Pipeline (computing),Computer architecture,Parallel computing,Field-programmable gate array,Compiler,Application-specific integrated circuit,Stream processing,Embedded system | Journal |
Volume | Issue | ISSN |
9 | 2 | 1539-9087 |
Citations | PageRank | References |
18 | 0.93 | 23 |
Authors | ||
11 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mythri Alle | 1 | 87 | 8.34 |
| Keshavan Varadarajan | 2 | 106 | 9.96 |
| Alexander Fell | 3 | 66 | 8.66 |
| Ramesh Reddy C. | 4 | 18 | 0.93 |
| Nimmy Joseph | 5 | 18 | 0.93 |
| Saptarsi Das | 6 | 43 | 5.64 |
| Prasenjit Biswas | 7 | 142 | 17.06 |
| Jugantor Chetia | 8 | 34 | 2.30 |
| Adarsh Rao | 9 | 18 | 0.93 |
| S. K. Nandy | 10 | 320 | 50.83 |
| Ranjani Narayan | 11 | 155 | 21.06 |