Heterogeneous multi-core processors are attracted by the media processing applications due to their capability of drawing strengths of different cores to improve the overall performance. However, the data transfer bottlenecks and limitations in the task allocation due to the accelerator-incompatible operations prevents us from gaining full potential of the heterogeneous multi-core processors. This paper presents a task allocation method based on algorithm transformation to increase the freedom of task allocation. We use approximation methods such as CORDIC algorithms to map the accelerator-incompatible operations to accelerator cores. According to the experimental results using HOG descriptor computation, the proposed task allocation method reduces the data transfer time by more than 82% and the total processing time by more than 79% compared to the conventional task allocation method.

Heterogeneous multi-core architectures with CPUs and accelerators attract many attentions since they can achieve power-efficient computing in various areas from low-power embedded processing to high-performance computing. Since the optimal architecture is different from application to application, finding the most suitable accelerator is very important. In this paper, we propose an FPGA-based heterogeneous multi-core platform with custom accelerators for power-efficient computing. Using the proposed platform, we evaluate several applications and accelerators to identify many key requirements of the applications and properties of the accelerators. Such an evaluation is very important to select and optimize the most suitable accelerator according to the requirements of an application to achieve the best performance.

Accelerator cores in low-power embedded processors have on-chip multiple memory modules to increase the data access speed and to enable parallel data access. When large functional units such as multipliers and dividers are used for addressing, a large power and chip area are consumed. Therefore, recent low-power processors use small functional units such as adders and counters to reduce the power and area. Such small functional units make it difficult to implement complex addressing patterns without duplicating data among multiple memory modules. The data duplication wastes the memory capacity and increases the data transfer time significantly. This paper proposes a method to reduce the memory duplication for window-based image processing, which is widely used in many applications. Evaluations using an accelerator core show that the proposed method reduces the data amount and data transfer time by more than 50%.

Accelerator cores in low-power heterogeneous processors have on-chip local memories to enable parallel data access. The memory capacities of the local memories are very small. Therefore, the data should be transferred from the global memory to the local memories many times. These data transfers greatly increase the total processing time. Memory allocation technique to increase the data sharing is a good solution to this problem. However, when using reconfigurable cores, the data must be shared among multiple contexts. However, conventional context partitioning methods only consider how to reuse limited hardware resources in different time slots. They do not consider the data sharing. This paper proposes a context partitioning method to share both the hardware resources and the local memory data. According to the experimental results, the proposed method reduces the processing time by more than 87% compared to conventional context partitioning techniques.

Dynamically-programmable gate arrays (DPGAs) promise lower-cost implementations than conventional field-programmable gate arrays (FPGAs) since they efficiently reuse limited hardware resources in time. One of the typical DPGA architectures is a multi-context FPGA (MC-FPGA) that requires multiple memory bits per configuration bit to realize fast context switching. However, this additional memory bits cause significant overhead in area and power consumption. This paper presents novel architecture of a switch element to overcome the required capacity of configuration memory. Our main idea is to exploit redundancy between different contexts by using a fine-grained switch element. The proposed MC-FPGA is designed in a 0.18 µm CMOS technology. Its maximum clock frequency and the context switching frequency are measured to be 310 MHz and 272 MHz, respectively. Moreover, novel CAD process that exploits the redundancy in configuration data, is proposed to support the MC-FPGA architecture.

This paper presents a novel architecture to increase the hardware utilization in multi-context field programmable gate arrays (MC-FPGAs). Conventional MC-FPGAs use dedicated tracks to transfer context-ID bits. As a result, hardware utilization ratio decreases, since it is very difficult to map different contexts, area efficiently. It also increases the context switching power, area and static power of the context-ID tracks. The proposed MC-FPGA uses the same wires to transfer both data and context-ID bits from cell to cell. As a result, programs can be mapped area efficiently by partitioning them into different contexts. An asynchronous multi-context logic block architecture to increase the processing speed of the multiple contexts is also proposed. The proposed MC-FPGA is fabricated using 6-metal 1-poly CMOS design rules. The data and context-ID transfer delays are measured to be 2.03ns and 2.26ns respectively. We achieved 30% processing time reduction for the SAD based correspondance search algorithm.

This paper presents a high-level synthesis approach to minimize the total power consumption in behavioral synthesis under time and area constraints. The proposed method has two stages, functional unit (FU) energy optimization and interconnect energy optimization. In the first stage, active and inactive energies of the FUs are optimized using a multiple supply and threshold voltage scheme. Genetic algorithm (GA) based simultaneous assignment of supply and threshold voltages and module selection is proposed. The proposed GA based searching method can be used in large size problems to find a near-optimal solution in a reasonable time. In the second stage, interconnects are simplified by increasing their sharing. This is done by exploiting similar data transfer patterns among FUs. The proposed method is evaluated for several benchmarks under 90 nm CMOS technology. The experimental results show that more than 40% of energy savings can be achieved by our proposed method.

The large data-transfer time among different cores is a big problem in heterogeneous multi-core processors. This paper presents a method to accelerate the data transfers exploiting data-transfer-units together with complex memory allocation. We used block matching, which is very common in image processing, to evaluate our technique. The proposed method reduces the data-transfer time by more than 42% compared to the earlier works that use CPU-based data transfers. Moreover, the total processing time is only 15 ms for a VGA image with 1616 pixel blocks.

For an FPGA-based heterogeneous multicore platform, we present the design methodology to reduce the total processing time considering data-transfer. The reconfigurability of recent FPGAs with hard CPU cores allows us to realize a single-chip heterogeneous processor optimized for a given application. The major problem in designing such heterogeneous processors is data-transfer between CPU cores and accelerator cores. The total processing time with data-transfers is modeled considering the overlap of computation time and data-transfer time, and optimal design parameters are searched for.