As a simple five-stage General-Purpose Processor (GPP), the baseline FlexCore processor has a limited set of datapath units. By utilizing a flexible datapath interconnect and a wide control word, a FlexCore processor is explicitly designed to support integration of special units that, on demand, can accelerate certain data-intensive applications. In this paper, we propose the integration of a novel Double Throughput Multiply-Accumulate (DTMAC) unit, whose different operating modes allow for on-thefly optimization of computational precision. For the two EEMBC benchmarks considered, the FlexCore processor performance is significantly enhanced when one DTMAC accelerator is included, translating into reduced execution time and energy dissipatio...
Large MIMO base stations remain among wireless network designers’ best tools for increasing wireless...
This paper presents the architecture of a flexible and high performance unit for DSP applications. T...
International audienceMachine learning algorithms are compute-and memory-intensive. Their execution ...
As a simple five-stage General-Purpose Processor (GPP), the baseline FlexCore processor has a limite...
Due to diversified demands of customers, embedded processor datapaths have been extended to accept m...
A proven approach to increase performance of general-purpose processors is to add hardware accelerat...
We propose a high-speed and energy-efficient two-cycle multiply-accumulate (MAC) architecture that s...
The comfort of our daily lives has come to rely on a vast number of embedded systems, such as mobile...
The FlexCore processor is the resulting implementation of an exposed datapath approach conceptualize...
We introduce FlexCore, the first exemplar of an architecture based on the FlexSoC framework. Compris...
We introduce FlexCore, which is the first exemplar of a processor based on the FlexSoC processor par...
Abstract—The FlexCore processor is the resulting implementation of an exposed datapath approach conc...
Tailored to run domain-specific applications under very strict constraints on, for example, real-tim...
The design of an embedded processor is dependent on the application domain. Traditionally, design so...
Fine-grained control through the use of a wide control word can lead to high instruction-level paral...
Large MIMO base stations remain among wireless network designers’ best tools for increasing wireless...
This paper presents the architecture of a flexible and high performance unit for DSP applications. T...
International audienceMachine learning algorithms are compute-and memory-intensive. Their execution ...
As a simple five-stage General-Purpose Processor (GPP), the baseline FlexCore processor has a limite...
Due to diversified demands of customers, embedded processor datapaths have been extended to accept m...
A proven approach to increase performance of general-purpose processors is to add hardware accelerat...
We propose a high-speed and energy-efficient two-cycle multiply-accumulate (MAC) architecture that s...
The comfort of our daily lives has come to rely on a vast number of embedded systems, such as mobile...
The FlexCore processor is the resulting implementation of an exposed datapath approach conceptualize...
We introduce FlexCore, the first exemplar of an architecture based on the FlexSoC framework. Compris...
We introduce FlexCore, which is the first exemplar of a processor based on the FlexSoC processor par...
Abstract—The FlexCore processor is the resulting implementation of an exposed datapath approach conc...
Tailored to run domain-specific applications under very strict constraints on, for example, real-tim...
The design of an embedded processor is dependent on the application domain. Traditionally, design so...
Fine-grained control through the use of a wide control word can lead to high instruction-level paral...
Large MIMO base stations remain among wireless network designers’ best tools for increasing wireless...
This paper presents the architecture of a flexible and high performance unit for DSP applications. T...
International audienceMachine learning algorithms are compute-and memory-intensive. Their execution ...